Advanced EEG Signal P rocessin g w ith Applications in
Brain-Computer Interf aces

Evaluating user focuse d paradigms for t he purpose of enhancing Brain -
Computer Inte raction

vo rge le gt von
M. Sc.
I r ina-Emili a N i colae
ORCI D : 0000-0002-9346-8467

von der Fa k u lt ä t I V - El e ktrot ec hnik und Inform a tik
der T ec hni s chen U niv ers i tät Be r lin (TUB)
un d der Fa k u lt ä t f ü r E lekt r onik , Tele ko mm u nikation un d Info rm ation st echnolo g ie
der Poly tec hni s che n U niv ers itä t Bu k arest (PU B)
(im Ra h m en de s Do pp e l -Pr omo t ion s ab ko mme n s)

zur Erl a n gu n g d es a k ade m i s c h e n Grades

Dok t o r der In g e ni e urw i sse n sc h a f t en
– Dr.-Ing. –

geneh m i g te D i ssertat ion

Pro m o t ion s aussch uss :

Vo rs i tze n der ( PU B) : Prof . D r. In g. G heo rg he Brezeanu
Vo rs i tze n der (T U B) : Prof . D r. Manf r ed Opper
Gutach ter : Prof . D r. In g. Da n A. St oich escu
Gutach ter : Prof . D r. Benj a min B la nk ert z
Gutach ter in : Prof . D r. In g. M ihaela Neagu (U n gur ea n u)
Gutach ter : Prof . D r. Klaus-Ro b ert Mü ller
Gutach ter : Prof . D r. Med. Gabr i el Cur io
Gutach ter : Prof . D r. In g. M iha i Iv anovic i

Ta g der w i ss enschaf t li c h e n Aussprach e : 2. Okt o ber 2018
an d er Polyt e chni s chen U nive rs i tä t Bu kare s t

Berlin , 2019

Pro iect KNO WLE DGE - POSD RU/1 59/1 .5/ S/13 4398
Dezvo lta rea resu rse lor u man e din ce rceta rea d octo ra lă și po stdoc to rală: moto r a l soc ie tății baza te p e cu noa ște re

UNIVERSIT ATEA POLITEH NICA DIN BUCUR EȘTI
Facultat ea: Electronică , T ele comunicații și Tehnolog ia Informației
Departm entul: Electro nică Aplicat ă și Ing ineri a I nformației
TECH NISCHE UNIVERSITÄT BER LIN
Faculty: E lectrical Enginee ring and Com puter Science
I nsti tut: S oftware Engineering and Theoretica l Com puter Science

Nr. Decizie Senat: 271 din 31.08.2018

TEZĂ DE DOCTORAT

METO DE AVANSATE DE PRELUCR ARE A SEM NALELOR EEG CU APLICAȚII
ÎN BRA IN COMPU T ER INTE RFACES
Evaluarea p aradigmelor orientate c ătre utilizatori cu scopul îmb unătățirii
I nteracțiu nii Creier-Calculator

ADV ANCED EEG SIGNAL PROCESSIN G WITH APPL ICATIONS IN
BRAIN-COMPU TER INT ERFACES
Evaluating u ser focused pa radigms for th e purpose of enha ncing Brain -Compute r
Interactio n

Autor: Ing. Irina-E milia NICOLA E

COMISIA DE DOCTORAT

Preşed inte

Pro f. Dr. Ing. Gheorghe
BREZEANU

de la

Universitatea Po litehnica București

Conducător de d octorat 1

Pro f. Dr. Ing. Dan A.
ST OICHESCU

de la

Universitatea Po litehnica București

Conducător de d octorat 2

Pro f. Dr. Benjam i n
BLANKERT Z

de la

Tec hn ische U niversität Ber lin

Referent

Pro f. Dr. Ing. Mihaela
NEAGU (UNGU RE A NU)

de la

Universitatea Po litehnica București

Referent

Pro f. Dr. Klaus-Robert
MÜLLER

de la

Tec hn ische U niversität Ber lin

Referent

Pro f. Dr. med. Gabriel
CURIO

de la

Charité – Universitäts medizin B erlin

Referent

Pro f. Dr. Ing Mihai
IVANOVICI

de la

University T ransilvania of Bra șov

Bucureşti 201 8

i

To the memo ry of my beloved father, N icolae Flori n

iii
ABSTRACT (EN)
(English)
A dvances in signal processing push forward the Neurotechnolog y domain along with the
Bra in-Computer Interface (BCI ) re s earch which deals with the anal y sis of br ain activit y .
Heading for a future that will most probabl y happen, where either healthy persons or people
with disabilities communica te and control external devices without muscle control, a
symbiotic relationship b etwee n humans and machines needs to be c rea t ed. Moreover, the
research direction should be g uid ed to the users’ si de by evaluating users’ interest s and ne eds.
The main g oal of this t hesis is to provide su gge stions and solutions to ea se and
facilitate the Brain-Computer I nt eraction, by the following: i) stimuli and tasks that refer to
users’ mental states and interests are optimized; ii) an interpre table system is crea ted to reveal
the neural information that c an further determine a controlled BCI s y stem to act; iii) and the
most important aspect that make the first two key points possible: advanced and improved
methodologica l approaches are develope d to efficientl y extract and interpret human neural
activity from the Elec tro ence ph alogram (EEG).
The investigation is performed through two e x perimental studi es, where the first one
proposes improved sti muli and tasks regarding users’ interests and pr efer ences in a motor-
imagery- ba s ed BCI. The sec ond stud y c onsiders users’ cognitive mental states with the
purpose to better control BCIs and investigates not onl y what th e use r h as received from the
external information, but also how a nd to which level of pro cessing is the information
encoded within the b rain. The paradigms investi ga te the brain flu ctuations induced b y
differe nt stimuli and tasks, in order to provide t he means to s ilen tl y d etect the mea nin gful
neural information from the brain activit y , which is critica l for a BCI application. While the
first paradigm considers Sensorimotor Rh y thms (SMRs), the second p aradigm is based on
Event Related P otentials (ERPs). Most B CI p aradigms consider either the temporal or the
spectral info rmation of the generated bra in activit y , but infrequentl y the investigation is
performe d in ensemble considering both domains. As it will be observed in this work, the
analy sis pipeline that c o nsiders onl y one domain might be suboptimal, while brain activit y
manifests additional information which is visib le in both temporal and spectral domain s.
Therefore, this thesis deals with the methodologica l improvements that include
complementary information, y ielding to more accurate data anal y sis that outperforms most of
the available me thods.

v
ZUSAMMENFASSUN G (GE)
(German)
F ortschritte auf dem Gebiet der Signalverarbeitung beeinflussen auch die Entwicklung en (in
der Neurotechnologie un d somit auch die Erfor s chung) der Gehirn -Computer Schnittstellen
(BCIs). Um Menschen mit körperlichen Einschränkungen, wie auch gesunden Menschen, die
Möglichke it zu geben oh ne muskuläre Kontrolle über externe G eräte z u k ommunizieren oder
diese zu kontrollieren, muss eine sy mbiotische Beziehung z wischen Mensch und Maschine
ge s chaffen w erden. Hie rfür sollte in der Forschung insbesondere ein größerer Fokus auf die
Interessen und Bedürf nis se der Nutzer:innen ge l egt werden.
Das Ziel dieser Arbeit ist e s Lösungsvorschläge für eine verbesserte Gehirn -
Computer-Intera ktion zu untersuchen. Dabei we rden: i) Stimuli u nd Aufgabenstellungen die
sich auf d en mentalen Zustand der Nutzer:innen beziehen optimiert, ii) ei n interpretierbares
BCI geschaffen, um die entscheidenden neuronalen I nformationen zu bestimm en, iii) die
beiden ersten Punkte we rden vor allem durch verbess erte methodische Ansätze e rmöglicht
welche effizient neuronale Aktivitäten vom Elektroenzephalogramm (EE G) extrahieren un d
interpretieren.
Hierfür werden zwei EEG Studi en ana l ysier t. Erstere untersucht verbesserte Stimuli
und Aufgabe nstellun gen bezüg lich d er Nutzerinteressen in einem motor -imag er y basierten
BCI. Die zweite Studi e analysiert kognitive Z ustände um hera uszuf inden wie externe
Informationen im Gehirn ankommen und wie di ese verarbeitet werden. Die beiden Studien
untersuchen die Fluktuationen im G ehirn welche dur ch unterschiedl iche Stimul i und
Aufgabe nstellun gen induz iert werden, um aussa gekrä fti ge neuronale Informationen, welche
für die Anwendung des B C I wichti g sind, zu bestimm en. W ähre nd das erste Paradigma die
sensormotorischen Rh ythmen (SMRs) betrachtet, basiert das z weite Par adig m a auf
ereigniskorrelierte n Potentialen (ERPs). Die mei sten BCI Paradigmen betrachten entweder
die zeitliche ode r die spektrale Domäne der Gehirnaktivität, eher s elten werden beide im
ensemble analysiert. In dieser Dis sertation komm en wir zu dem Schluss, dass die Anal y se die
sich nur auf eine der bei den Domänen stützt nicht optimal ist , da wichtige I nformationen in
beiden Domänen enth alten ist. Deshalb an aly sie ren wir e rweiterte Methoden die
komplementäre Informationen aus beiden Domänen kombinieren, was zu einer genaueren
Datenanaly s e führt, die die Erge bnisse der bisheri ge n M ethoden übertrifft.

vii
REZUMAT (RO)
(Rom ani an)
P rogre s ele în analiza semnalelor im pulsionează domeniul neuro - tehnologiei împreună cu
cercetarea Interfețelor Creier -Calculator (en., Brain-Computes Inter f ace s - BC I ) care se ocup ă
cu analiza activității cerebra le. Îndreptându- ne c ătre un vii tor ca re cel mai probabil se va
întâmpla cât de curând, î n care fie persoane sănătoase , fie persoane cu handica p comunică și
controlează dispozitive externe fără intermediul controlului muscular, o relație simbiot ică
între oameni și mașini tr ebuie să fie creată. Mai mul t, direcția de cercetare a r trebui să fie
ghidată către utilizator i, prin evaluarea intereselor ș i nevoilor utilizatorilor.
Scopul principal al ac estei lucrări este de a oferi suge stii și soluții pentru a ușura și
facilita interacțiunea creier-calculator , prin următoarele: i) stimul i i și activitățile care se referă
la stările m entale și int eresele uti lizatorilor, sunt optimizate; ii) un sistem interpretabil este
crea t pentru a dezvălui informația neuronală ce poate determina în continuare un sis tem de tip
BCI pe bază de control să acționeze; iii) ș i cel ma i important aspect care face posibile primele
doua puncte cheie: abordări metodologice avansate și îmbunătăț ite sunt dezvoltate pentru a
extrage ș i interp reta, în mod eficient, activitatea neuronală u mană relevată de
Electroencefalogra mă (EEG).
Investigarea se realizează prin două studii experimentale, în ca r e prim ul propu ne
stimuli și sarcini îmbunătățite privind interese l e și preferințele utilizatorilor în cadrul unei
Interfe ț e C reier -Calculator bazate pe imaginare mot orie. Al doilea studiu consid eră stările
mentale cognitive ale utili zatorilor vizând îmbunătățirea ulterioară a co ntrolului în cadrul
I nterfe ț elor Creier -Calculator și investig heaz ă nu numai ceea ce uti lizatorul a prelucrat din
informațiile externe, ci și modul și nivel ul de prelucrare al informației c odificate în cre ier.
Paradig m ele investighează fluctuațiile creier ului i nduse de diferiți stimuli și activități , pentru
a oferi mijloacel e de a detecta inform ația neuronală semnificativă din activitatea creierului,
care este critică pentr u o aplicație de tip BCI . În timp ce prima paradigmă consider ă ritmurile
sensori-motrice (SMR s ), a doua paradigmă se bazează pe p otențiale legate de evenimente
(en., Event-Related Potentials - ERPs). Maj oritatea paradigmelor B C I consider ă fie
informațiile temporale , fieinforma țiil e spectra le ale activității generate de că tre creier, însă
rare o ri cercetarea s e realiz ează în ansamblu, considerând ambele domenii , timp și frecvență .
Așa cum se va observă în această lucrare, analiz a care consideră un singu r dome niu ar putea
fi suboptimală, deoarece a ctivitatea creierului prezintă informații supl imentare ce sunt
vizibile atât în domeniul temporal, cât și în cel spectral. Prin urmare, a ceastă teză se ocupă cu
îmbunătățirile metodologice c e includ info rmații le compl ementare, obți nând o analiză mai
precisă a datelor c e depășește performanțele major ității metodelor disponibile.

ix
ACKNOWLEDGEMENTS

L ook ing back fr om whe n I firs t to ok a st ep on the pa th to m y PhD I am ve ry gra te ful for al l the
su pp or t and ad vice I ha ve r ece ive d a long the way . F ir st, I wa n t to e xpr ess my deep app rec ia tio n
a nd tha nks t o my su pervi so rs Pr of. D r. Ro dic a Stru nga ru an d Pro f. Dr . Be nj am in B lank ert z
wh o dedi ca ted p rec io us time and e ffor t to g uide and s upe rv ise the depl o y ment an d prog res s of
my the si s, a dd ing v alua ble s ugg es tio ns a nd i deas fr om the ir exp er tise to im pr ove o r c orre c t
e very de ta il of the s cien tif ic r esea rc h tha t ma de p oss ible the w or k pres en te d here , today .
Ad di tio na lly , s pec ial tha nks to t he Prof . Dr. Da n Stoi ch esc u, for g uid ing an d super vi sing the
ne ce ssa rie s to war ds the pub lic de fen ce, due to Pr of . Dr . R odic a S tru nga ru una vai lab il ity .
F oll ow ing, I wan t to than k to m y a dvi sor Prof . Dr. Mih ae la Neagu (Ung ure an u) fo r
c ont inu ous l y su pp orti ng m e wi th he lpf ul re vie ws, i mpr ove men ts a nd c om me nts al ong th e
re se arc h of th is c urre nt pi ece of w ork. Humb le th an ks to the dis ser tati on re fe ree s fo r the ir
va lu abl e re vie w a long with pr ec iou s co mme nts , sug ge st ion s, gui dan ce and add iti on al que sti on s
th at he lped cla rify ing uncl ear as pec ts of th e thes is. Th us, sp eci al than ks are ad dre sse d to Pr of .
Kl au s-R obe rt Müll er an d Pro f. Dr . Gab rie l Cur io. L ikew is e, I of fe r m y gra ti tud e to Pro f. Dr .
Ma nfr e d Op per fo r h is i mpo rt an t gui da nce and su pp ort ive que sti on s as a c ha ir o f the
pr el imi nary def enc e in Ber li n, and fo r hi s ava il ab ility on shor t no tice in resp on se to Pro f. Dr.
He nn ing Spre ke ler una vai lab il ity. Fur the r, I wan t to gi ve tha nk s t o Pro f. Sev er Pașc a fo r hi s
pr ec iou s co mme nts a nd obs er vat io ns as a sec on d advi so r of m y the sis . Ne xt, I want to tha nk to
L aura Ac qua lag na who br oug ht a lot of help an d gui da nce alo ng the dev el opme nt of m y
re se arc h.
Mo re over , I wan t to sin cere l y th ank to al l th e gr ea t pe opl e t hat help ed me a long this
ro ad wi th th e ir hel pful co mm en ts, s ugg est io ns a nd i dea s. F ro m the Ne uto rec hn ol ogy and
Ma ch ine L earn ing Dep ar tme nt s of Tech ni sch e Un iver si tä t B erli n, namely Mar ija , Ma rku s,
Ha n- Jeo ng, Pie ter -Ja n, Sve n, Ste fan , Mano n, I re ne S, I re ne W, Alex, Ste pha nie , Da nie l,
Ma tt hia s, M iria m and a ll the o the rs tha t I did n ot men ti on her e. T han ks a lso t o And re j a nd
Na ve en for thei r int ere st and co lla bora tion . As wel l tha nk s to Dom ini k, I mke and An drea for
th ei r a dmi ni stra ti ve an d te c hnica l su pp ort . Same than ks fo r the col leag ues fro m the De par tm ent
of App lie d Ele ct ron ics an d I nfo rma ti on E ngin eer ing , Uni ver si t y Pol ite hni ca of Buc ha res t,
a mong whic h I re cal l B ogda n, Drag oș , Mir una , Roxan a, Andre ea, Cor ina and may be many
ot he rs. Al so, th ank s to I ng . Nic ole ta Br ăniș tea nu a nd Pro f. Ghe org he Bre zea nu for their
g ui danc e con side ri ng the ad mi nis tra tive PhD issue s. Fu rth er, I th ank to all my co- au thor s and
c oll abo rat or s wh ich I d id no t men tio n a lre ady , for th e g rea t tea m wor k perf or med w ith in the
pr oj ect s that I par ti cipa te d d ur ing m y Ph D.
Further, I address man y thanks for all the participants who dedicated precious ti me for
being involved in the experiments and for havin g lot of patience and endurance in the te dious
preparation of the experiments b y mountin g the EEG cap and tolerating the sti cky gel on their
hair. Among all, the scientific experiments would not be made possible without them.
We ac knowledge fin ancial support from the Sectoral Operational Programme Human
Resources Development 2007 -2013 of the Mini stry o f Eu ropean Funds through th e Financial
Agreement POSDRU/159/1.5/S/134398 . Further, the work leadin g to these results was
sponsor ed by the Eu ropean Union Seventh Framework Programme ( FP7/2007-2013) under
grant agreement no. 611570 and completed by the BMB F under co ntract 01GQ0850.
Considering publication funds, we acknowledge support by the German Research Foundation
and the Open Access P ublication F unds of Technische Universität Berlin, and Prof. Dr.
Mihaela Neagu (U n gureanu).

Contents
xi
Contents

ABSTRACT (EN) ......................................................................................................... iii
ZUSAMMENFASSUNG (GE) ..................................................................................... v
REZUMAT (RO) ......................................................................................................... vii
ACKNOWLEDGEMENTS .......................................................................................... ix
Contents ........................................................................................................................ xi
List of figures ............................................................................................................... xv
List of tables ................................................................................................................ xix
List of a bb reviations ................................................................................................... xxi
Introduction .................................................................................................................... 1
1.1 The field of doc to ral study .......................................................................................... 1
1.2 Purpose of the thesis .................................................................................................... 3
1.3 Outline of the thesis..................................................................................................... 4
1.4 Scientific contribution ................................................................................................. 6
1.5 List of c ontributions .................................................................................................... 8
1.5.1 Main contributions list ......................................................................................... 9
1.5.2 Additional contributions list .............................................................................. 10
Fundamenta ls of n europhy s iolog y ............................................................................... 13
2.1 Neurophysiolog i cal background ............................................................................... 13
2.2 Electroencepha lo graphy ............................................................................................ 14
2.3 Bra in- Co mputer Interfa c e ......................................................................................... 14
2.4 Neurophysiolog i cal s ignals that enable BCI control ................................................. 15
2.4.1 Event Related Potentials .................................................................................... 16
2.4.2 Os cillatory brain activity.................................................................................... 17
2.5 Notation ..................................................................................................................... 21
Signal processing and machine learning methods in BCI re s earch ............................. 23
3.1 Bra in activity measurement ....................................................................................... 23
3.2 Preproce ssin g ............................................................................................................ 24
3.2.1 Preliminary filte ring and preprocessing ................................ ............................. 24
3.2.2 Enhanced artifact re mov al ................................................................ ................. 25
3.3 Fe ature extraction ...................................................................................................... 33
3.3.1 Fea tur e selection and dimensionalit y reduction ................................ ................. 34

Contents
xii
3.3.2 Temporal methods ............................................................................................. 34
3.3.3 Spectral methods ................................................................................................ 35
3.3.4 Time-fre qu ency measures ................................................................ .................. 36
3.4 Classification and Regre s sion ................................................................................... 37
3.4.1 Linear Discriminant Analysis (LDA) ................................................................ 38
3.4.2 Binomial L o gistic Regression (LR) ................................................................... 40
3.4.3 Multinomial L o gistic Regression (MLR) .......................................................... 40
3.4.4 Regression .......................................................................................................... 40
3.4.5 Classification validation..................................................................................... 41
3.4.6 Classification evaluation .................................................................................... 41
3.5 BCI applica tions ........................................................................................................ 43
3.5.1 Applications for ERP-based paradigms ............................................................. 44
3.5.2 Motor-imagery ba s ed BCIs applications ........................................................... 44
3.5.3 Mental state B C Is applications .......................................................................... 44
Revealing th e neural correlates of user efficient motor imagery tasks ........................ 47
4.1 Introduction and state of the art................................................................................. 47
4.2 Methods ..................................................................................................................... 49
4.2.1 Experimental design a nd scenarios f o r efficient user motor imagery tasks ....... 49
4.2.2 Bra in si gnal acquisition and equipment ............................................................. 51
4.2.3 Processing str ategy to detect the spec ific motor im agery neu ral corre l ates ...... 52
4.3 Findings ..................................................................................................................... 53
4.3.1 Beha vio ral data .................................................................................................. 53
4.3.2 Filtering m easures comparison .......................................................................... 54
4.3.3 Detecting the ne u ral correlates of specific motor imagery tasks ....................... 54
4.3.4 Classification of specific motor imagery tasks .................................................. 58
4.4 Discussion and conclusions ....................................................................................... 61
4.4.1 Comparison with previous studies ..................................................................... 62
4.3.5 Open questions and conclusions ........................................................................ 63
4.5 Limitations and future d evelopments ........................................................................ 64
4.6 Le ssons le arned ......................................................................................................... 67
Investigating the n eural correlates of cognitive processing l evels ............................... 69
5.1 Introduction and state of the art................................................................................. 69
5.2 Methods ..................................................................................................................... 71

Contents
xiii
5.2.1 Experimental design to elicit different cog nitive p rocessing leve ls .................. 71
5.2.2 Neural signals acquisition .................................................................................. 75
5.2.3 Analy sis strat egy to detect the neural correlates of different levels of co gnitive
processing ........................................................................................................................ 75
5.3 Findings ..................................................................................................................... 83
5.3.1 Investigating b ehavioral measures ..................................................................... 83
5.3.2 Extracting the ne ur al correlate s fo r each cog nitive processing leve l ................. 84
5.3.3 Discrimination of different cog nitive pro cessing leve ls .................................... 95
5.4 Discussion and conclusions ..................................................................................... 103
5.4.1 Comparisons with other studies ....................................................................... 103
5.4.2 Open questions and conclusions ...................................................................... 104
5.5 Limitations and future di rections ............................................................................ 110
5.6 Le ssons le arned ....................................................................................................... 112
Conclusions ................................................................................................................ 113
6.1 General conc lusi ons ................................................................................................ 113
6.2 Future perspectives towards BCI a pplic ations ........................................................ 115
Appendix .................................................................................................................... 117
A.1 Additional theoretica l foundations – Chapter 2 ...................................................... 117
A.1.1 Statistical analy sis ............................................................................................ 117
A.1.2 Measure s o f signal quality ............................................................................... 120
A.1.3 Signal processing ................................ ............................................................. 120
A.1.4 Reg ula rize a discriminant analy sis cl assifier (Matlab implementation) .......... 125
A.2 Supplementary m aterial for the motor image r y stu dy – Chapter 4 ......................... 127
A.2.1 EEG ac tivit y detec tion ..................................................................................... 127
A.2.2 EMG activity d etection .................................................................................... 127
A.3 Supplementary m aterial for the De pth of cognitive proc essin g study – Chap ter 5 . 131
A.3.1 Artifac tual components .................................................................................... 131
A.3.2 ER P analysis .................................................................................................... 133
A.3.3 ERD/ERS analysis ................................................................ ........................... 137
A.3.4 Discriminatory anal ysis ................................................................................... 138
Bibliography ................................ .............................................................................. 143

Contents
xiv

List of figures
xv
List of figures

Fig. 4.1 Timing for one experimental trial with right movement execution in this example . 50
Fig. 4.2 Power Spe ctral Densit y Estimate using the W elch method for the original sign al and
filtered sig n al ………………………………………………………………………………. 54
Fig. 4.3 Mean amplitude evolution over time f or the a rm lifting motor ima g er y task as
response to Vis.-So. St ……………………………………………………………..……… . 55
Fig. 4.4 Event related sp ectra l p erturbation fo r the ri g ht/left im age r y tri gger pull (Vo. St.) at
electrode s C3 and C4 … ……………………………………… . …………………………… 56
Fig. 4.5 Inter-Trial Cohe renc e (ITC) for the left imager y trigger pull (Vo. St.) at electrode C3
……………………………………………………………………………………………… 56
Fig. 4.6 ERSP at the C3 and C4 electrodes for t he right index fing er mo tor imager y button
press task (Vo. St.) ……………………………………………….………………………… 57
Fig. 4.7 Temporal evol ution of the ERP components (average data) consi dering the right
imagery button pre ss wit h Vo. St. ………………………...……………………………… ... 57
Fig. 4.8 I CA components contributions to ER P and ERSP, considering the lef t imag er y
button press task (Vis. St.) …………………………….………………………………… .... 58
Fig. 4.9 Th e performance of the classifier given by the error rate, in relation to the number of
features ……………………………………………………………………………………... 59
Fig. 4.10 Average p erformanc e for multi -class and binar y classification for the motor
imagery t asks: button press (Vis. St.), button press (V is. -So. S t.), arm lift ing (Vis.-So. St.),
button press (Vo. St.), trigg e r pull (Vo. St.) ………………………………..… . …………… 60
Fig. 4.11 Normalized confusion matrices over entire dataset for the multi -class motor
imagery c lassific ation considering the no, left and right move ments …… . …… .. …….…… 61
Fig 5.1 Repre sentation of the stimuli categ ories (animals, fruits, mobility) and examples of
their elements ………………………………………………………………………………. 72
Fig. 5.2 Experimental protocol examples for the cognitive pro cesses investigated: memory,
language a nd visual ima gination …………………………………………………………… 73
Fig. 5.3 Example of elements size indicators …………………………………………….... . 74
Fig. 5.4 Timi ng of the trial: 500 ms for the fix ation cross, 1250 ms for the stimulation period
and 750 ms for the relaxation period …………………………………..…………………… 75
Fig. 5.5 Data ana l ysis diagram …………………………………………… …… ... ………… 81
Fig. 5.6 Behavioral assesment – objective and subjective indicators fo r the Memor y (M),
La n guage (L) a nd Visual I ma g in ation condition (VI): answers r atio and di fficulty s core s ... 83
Fig. 5.7 Grand average ERPs. (a) Memor y condition; b) Language ; c) Visual imagination . 84
Fig. 5.8 Grand-average discriminations of the Event -Related Potentials given by si gne d r 2
considering Memory, L a ng uage and Visu al imag i nation conditions ……………..…… . ….. 85
Fig. 5.9 Grand average p ower spe ctrum at location P z, for the Deep (DT), shallow (ST) and
no-proce ssin g (NT) levels in case of me mor y , l anguage and visual imagination conditions . 86
Fig. 5.10 Grand-average spectrum discriminability given b y si gne d r 2 at location Pz … . …. 87

List of figures
xvi
Fig. 5.11 Grand-average ERDs for the alpha band (8-14 Hz) considering all conditions (M, L,
VI) and proce ssin g levels (NT, ST, DT) a t electrode Pz …….……………… …..………… 88
Fig. 5.12 Gra nd -average ERDs for the beta band (16 -20Hz) at electrode P z for all conditions
(M, L , VI) and processing levels (NT, ST, DT) …………………………………………… 89
Fig. 5.13 Binar y CSP analy sis (patterns) for the alpha frequenc y band (8 -14Hz) after SSD
considering NT-ST, NT-DT, ST-DT class pairs for each condition (Participant P4) .. ……. 90
Fig. 5.14 Binary CSP with SSD patterns for the b eta frequenc y b and (16 -2 0 Hz) considering
all conditions and classifica tion pairs (Participant P4) ……………………………….……. 91
Fig. 5.15 Multi-case CSP with SSD patterns of the 8-14Hz frequenc y band considering the
NT, ST and DT classes for eac h condition (Participant P4) ……………… . …….………… 92
Fig. 5.16 Multi-case CSP with SSD patterns of the 16-20Hz frequenc y b and considering the
NT, ST and DT classes for eac h condition (Participant P4) …… ............................. ......... … 93
Fig. 5.17 Spectrogram ( ERSP and ITC) for the memory process at channels F7, P3, and Pz ,
considering the NT, ST and DT levels … . ………………… ................. ………… . ………... 94
Fig. 5.18 Spe ctrogra m ( ERSP and I TC) for the language process at channels F7 and Pz ,
considering the NT, ST and DT leve ls …… . ……………… ........... ………….......... ............ 94
Fig. 5.19 Spectrogram (ER SP and I TC) for the visual imagination process at channels Cz, P3,
PO7, PO8 and Pz, considering th e NT, ST and DT levels .................................................... 95
Fig. 5.20 Pairwise classification mean performance over all trials for all cognitive processes
(memory, langua ge , visual imagination, light grey ) given b y the area under the ROC curve
(AUC) base d on ERP-mCSP ………………………………… ..................................... …… 96
Fig. 5.21 Multi-class spatio -tempo-spectral class ification performance (ER P & SSD -mCSP )
for all conditions: Memory (M), La n guage (L) a n d Visual I m agination (Vi) ……...… . …... 97
Fig. 5.22 Normaliz ed confusion matrix for the Memory, Language and Visual I ma gination
condition across, sho wing th e mul ti-class spatio -tempo-spec tr al classifi cation performance
(ERP & SSD-mCSP) betwee n no (NT), shallow (ST) and deep processing (DT) .. …… ...... 98
Fig. 5.23 Pair-wise spatio-temporal (ERP) classif ication perf o rmance between th e levels of
processing (N T – no -processing, ST – shallow processing, DT – deep processing) …… . … 99
Fig. 5.24 Pair-wise spa tio-spectral (SSD-mC SP) classific ation performance between the
levels of processing : NT – no-processing, ST – shallow processing, DT – de ep processing
…………………………………………………………………………..…………………. 100
Fig. 5.25 Th e classifi ers performance for the language condition considering the average
AUC values …………………………… ................................... ………… ........................... 101
Fig. A.1.1 Scaling and wavelet functions for the Daubechies wave let o f order 5 ............... 123
Fig. A.2.1 Scatter plot of C 3 vs C4 elec trodes showing the distribution of the imagery tri ger
pull data (Vo. St.) ……………………………………………………….…………… . …... .127
Fig. A.2.2 Average E MG signals over all tr ials for the left and ri g ht motor imager y
movement (finger button press experiment with Vis. St.) ……………… .......... ……… . … 128
Fig. A.3.1 Example of artifactual components de tected b y ICA with MARA for participant
P5 on the memory da t a, as compared to non-artifact neural component ............................. 132
Fig. A.3.2 Event-related potentials corresponding to the cognitive activit y depicted in the
temporal evolution of the EEG signal, at cha nn el CPz ……………………… ................ … 133
Fig. A.3.3 Temporal evolut ion of the trials and mean ERP corresponding to the memory
condition, for NT, ST and DT , at channel CP z, for pa rticipant P5 ….. ......... ....................... 134

List of figures
xvii
Fig. A.3.4 Temporal and spatial dist ribution of th e mean ERP of p articipant P5 for th e NT,
ST and DT processing le vels conidering the me m ory condition at electrode Pz ................. 134
Fig. A.3.5 Gr and avera g e ERPs for the DT+ and DT - levels considering the memor y,
language a nd visual ima gination conditions at electrode Cz … ........................... ………… 135
Fig. A.3.6 Gr and avera ge ERP considering all electrodes and all processi ng levels (NT, ST,
DT - and DT+) for the language condition …………………… .................. ………………. 135
Fig. A.3.7 Statistical temporal differences between classes considering signed r 2 at midline
channels for th e Memory, La n guage and Visual Imagination conditions .... ....................... 136
Fig. A.3.8 G rand-average ERD/ERS signed r 2 discriminabilit y b etween DT and S T for the
alpha (8-14 Hz) and the beta band (16-20Hz) at location Pz ............................................... 137
Fig. A.3.9 Grand average ERD/ERS curves on 8-14 Hz considering all electrodes and all
processing leve ls fo r the memory condition ……………………… ................ …………… 137
Fig. A.3.10 Gr and average ERD/ERS curves on 16-20Hz considering all electrodes and all
processing leve ls fo r the memory condition …………………………… ................ ……… 138
Fig. A.3.11 Scatter plot of P z vs Cz electrode s showing the distribution of the lan guag e data
considering the NT, ST and DT classe s ……………………………………... .................... 138
Fig. A.3.12 The normal distributi on graphs of the binar y ERP-mCS P classifier performance
……………………………………………………………………. ...................................... 140
Fig. A.3.13 The variance distribution graph of t he residuals between Language and Visual
imagination for th e NT-ST group, considering binar y ERP -mCSP classifier perf ormance. 140
Fig. A.3.14 The variance distribution graph of t he residuals between Language and Visual
imagination, considering the multi-class ERP-mCSP classifier perf o rmance ..................... 142

List of figures
xviii

List of tables
xix
List of tables

Ta b. 2. 1 N ota tio n ………… ……… …… …………… … ……… … …………… …… ……… . 21
Ta b. 4. 1 M ot or i mag ery exp er im ent ty pes …… ……… ……… …… ………… .. ………… . … 51
Ta b. 4. 2 S ign al qu ali ty esti ma tio n aft er fi lte ring , base d on the PS NR mea sure ………… .. … 54
Ta b. 4. 3 F eat ure s t y pe, orig inal fe atu res q uan tity a nd r ed uce d f eat ure s qua nti ty …… .. … …. . 59
Ta b. 5. 1 Com pa ri son over al l co nd iti ons bet wee n the mult i-c las s by JAD cla ss if ica tion
pe rf or manc e a nd the m ulti -c las s by OV R ap pro ac h ………………………… .. ... .. .... .. . …… . 102
Ta b. A. 3. 1 ERP an d mu lti ba nd CSP wi th SSD bina ry cla ssi fic at ion per form an ce wi th shr in k
rL DA ove r a ll co nd iti ons an d pa rtic ipa nts … ………… …… …… ………… .. …... …… …… 139
Ta b. A . 3. 2 E RP- mCS P wit h SSD (JA D a nd OV R) mu lt i-cl as s cla ss ific at ion p erf orm ance s w it h
sh ri nk rL DA ove r a ll c ond it ion s and par ti ci pan ts …………… … …………… …… …… ..…. 141

List of tables
xx

List of a bb reviations
xxi
List of ab breviations

Acc – Accuracy
AEP – Auditory Evoked Potentials
ANOVA – Anal y sis of V ariance
AUC – Area und er the ROC curve
BCI, B C Is – Brain-Computer Interface(s)
CDF – Cumulative Distribution Function
CMI – Continuous Motor I m agery
CSF, CSFs – Common Spatial Filter(s)
CSP, CSPs – C o mmon Spatial Pattern(s)
CV – Cross-Validation
CWT – Continuous Wavelet Tra nsform
DT – Deep Targets
DFT – Discrete Fourier Transform
DMI – Discrete Motor Imagery
EEG – Electroence ph alograph y
EMG – Electromyogra ph y
EOG – Electrooculography
ERD, ERDs – Event Related De s ynchronization(s)
RP , ERPs – Event Related Potential(s)
ERS, ERSs – Event Related Sync h ronization(s)
ERSP, ERPs – Event Related Spectral Perturbation(s)
FFDiag – Fast Frobenius Diag onaliz ation
FFT – Fast Fourier Tra ns form
FIR – Finite I mpulse Res ponse
fMRI – functional Magnetic Resonance Imaging
GA – Grand Average
GEVD – Genera liz ed Eigenvalue Decomposition
HP – high-pass filter

List of a bb reviations
xxii
IC , ICs – Independent Component(s)
ICA – Independent Component Analysis
II R – Infinite Impulse Response
IN – multi-class problem divi ded to severa l binar y dec isions
ISI – Inter-Stimuli I nte rval
ITC – I nt er-Trial Coherence
ITPC – Inter-Trial Phase Coherence
ITFE – I n formation Theoretic Feature Extraction
JAD – Joint Approximate Diagonalization
KS – Kolmogorov – Smirnov (test)
L – Language
LCD – Liquid-crystal-display
LDA – L in ear Discriminant Analysis
LPN – Low Pass Notch filter
LR – Logistic Regre ssio n
M – Memory
MARA – Multiple Artifact Rejec tion Algorithm
mCSP – Multi-Band Common Spatial Pattern
MEG – MagnetoEncephaloGraphy
MI – Motor I m agery
MI – Mutual Information
MLR – Multinomial Logistic Regre ssion
MSE – Mean Squared Error
NI RS – Near InfraRed Spectroscopy
NT – Non-Targets
OVR – One Versus Rest
PDF – Probability Density Function
PNG – Portable Network Graphics
PSD – Power Spectral Densit y
PSNR – Peak-Signal to Noise Ratio
QDA – Quadratic Discriminant Analysis
rLDA – Regular iz ed Linear Discriminant Analysis

List of a bb reviations
xxiii
ROC – Receiver Opera ti ng Characteristic
RR – Ridge Re gression
RMS – Root Mean Square
SDE – Spectral Density Estimation
SEM – Standard Error of the Mean
SI – I nternational S y st em of Units
SIM – Simultaneous Diagona liz ation
SMR – Sensorimotor Rhy t hms
SNR – Signal to Noise Ratio
SOA – Stimulus Onset Asy nchron y
SPoC – Source Power Co-Modulation
SSD – Spatio-Spectral Decomposition
ST – Shallow Targe ts
STFT – Short-Time Fourier Tr ansform
SVG – Scalable Vector Graphics
SVM – Support Vector Machine
TFR – Time-Freque n cy Representation
UTP – Unshielded Twisted Pair (cable)
USB – Universal Serial Bus
VEP – Visually Evoke d Potentials
VI – Visual Imagination
WAV – Waveform Audio File F o rmat
WT – Wavelet Tr ansform

List of a bb reviations
xxiv

1

Chapter 1

Introduction

Non-invasive Brain-Co mputer I nterfaces (BCIs) research b enefits f rom a sig nific ant
evolution in the last decades considering n eural activity anal y sis. The vast majorit y of
endeavors focus on identif y ing ascertained voluntary control commands, imposing strict
activities to the user side, in order to control distinct devices or for communication purposes.
On the other hand, the user state estimation from the ongoing brain si gnals was not granted
much attention (Blankertz et al., 2010b,c ; 2016 ).
1.1 The field of doc toral study
The enhancement of te chnolog y gives us toda y more and more oppo rtunities and utili ty to
ease and improve ou r dail y activities. In this re gard, n eurotechnology h elps further by
enhancing the connecti vity betw een humans and technology. It involves the participation of
differe nt fi elds such as Computer S cience , Neur oscience or Ps yc holo gy, and many others.
The applicabilit y ran g es from augmenting human capabilit y b y controlling external devices
such as computer applications, wheelchair or an y other electronic devices onl y with the brain
signals; to wards the restoration of a lost mot or or cognitive function b y neuro-rehabilitation
or even to the replacement o f a lost bod y part (mainly limbs ), with the aid of neura l
prosthesis.
The first advancements in this field began to develop with the discovery of human
brain signals, in 1924, b y th e German res earcher and ps yc hi atrist, Hans Berger (Ber ge r,
1929 ). After this ti me, multiple resea rches have been conducted and the field of
Neurotec hnolo gy evolved for nearl y h alf a century but onl y in the last twent y y ears has
reac h ed maturit y . In general, it includes technolo gies that are designed to improve , repair and
replace bra in fun ctions and allow researchers and clinicians to visualize the brain.
The neurotechnolog y research enhances step by step, mostl y related to B CI s y stems
(Dornhege et al., 2007 ; Wolpaw and W olpaw, 2012 ), providing enhancements of si g na l

Chapter I. Introduction
2
acquisition (Nicolas-Alonso and Gomez -Gil, 2012 ), sign al proc essing and mac hine le arning
techniques (Müller and Blankertz, 2006 ; Blankertz et al., 2008b ;). Furthermore, n ew
applications are d eveloped eac h d ay and new discoveries are being reported (Wol paw, 2007 ).
Although the main focu s of the BC I research initially t argeted c linical applications
relating to lost br ain fun ctions replacement, involving for example, patients who lost their
motor control, where the BCI provid es a different option to communication (Birbaumer et al.,
1999 ; Birbaumer and C ohen, 2007 ; Millán e t al., 2010 ) or an alterna tive to movement
execution b y means of BCI prosthesis (Birbau mer, 2006 ; Mcfarland and W olpaw, 2010 ) ,
various experimental paradigms that targets alleviating dail y activities have been also
proposed. The y are aiming to enhance the hu man capabilities of a n ormal and health y
individuals, for example as in the case of controlling a computer application with own brain
signals (Ba y liss and Ballard, 2000 ; Müller et al., 2008 ; Blanke rtz et al., 2010c ; Zander and
Kothe, 2011 ) and replacing the conventional peripheral input (e.g. mouse, key board) or in
industrial settings b y targeting workload reduction based on ment al state detection ( Venthur
et al., 2010 ).
While primary research involves the use of motor imag er y rel ated bra in activity for
BCI control, activity which is hard to be controlled by th e users, mostly because not ever y one
is capable o f producing this specific t y pe o f br ain signals (Guger et al., 2003 ; Blankertz et al.,
2010a ; Vidaurre et al., 2011b ), a new interest arises in the B C I community which focuses on
the user mental state detection, bringing many adv antages and decision possibilit ies for the
BCI system (Blankertz et al., 2016 ).
Another aspect that need to be take n into consideration when develo ping a BCI
system, considers the a n aly sis a nd decision s involved, that ha ve to be properl y check ed in
order to refer to the cor responding neural activit y relat ed to the BCI task and not to the non-
cortical origins of activity such as e y e, muscle m ovements and other t y pes of noise activity.
In this regard, a B C I s y stem depends on adv anced methods of signal processing and
classification. B y means of the se machine learning techniques, the corresponding neural
activity of int ere st is detected amon g a mix ture of neural signals , problem alway s referred to
as the ‘ cocktail party ’ problem (for ex ample, detecting and understanding one person's speech
from the amount of dis cussions , music and ba ckground noise which happen on a party
environment).
Despite significant advances in BCI research, t here is sti ll no standard valid BC I
system available on the market, but hopefully this is about to change in the decades to come,
thanks to the involvement of the BCI research groups through the e ntire globe ( Birbaumer
and Cohen, 2007 ; Dornhege et al., 2007 ; Kohlmorge n et al., 2007 ; Wolpaw, 2007 ; Daly and
Wolpaw, 2008 ; Galán et al., 2008 ; Mak and W olpaw, 2009 ; Müller et al., 2008 ; Ariely and
Berns, 2010 ; Haufe et al., 2011 ; Zander and Kothe, 2011 ; Wolpaw and Wolpaw, 2012 ;
Collinger et al., 2013 ; Hwa n g et al., 2013 ; B o rg hini et al., 2014 ; Aricò et al., 2016 a , b ;
Blankertz et al., 2016 ; Schultz e-Kraft e t al., 2016a , b ; Naumann et al., 2017 ), and recentl y a lso
the involvement of th e industr y s ector (Neur alink Corp - https://www.neuralink.com/ ;
Facebook, Inc. - Constine, ( 2017 , Apr 19) and many more.

1.2 Purpose of the thesis
3
1.2 Purpose of the t hesis
Th e most complex sy st em in the universe - the bra in, has fascinated researchers for a
long time. The abilit y to control ex ternal devices onl y with the power of the mind is still a
futuristic approach, y et man y studies have prov en thi s possibilit y and the most impor tant
aspect is that it is beneficial for the p ersons with disabilit ies, providing t hem the means fo r
communication in case o f loss of the abilit y to sp eech, the ability to control a wheelchair or
any oth er external device, which offers them a significant aid in their dail y activities.
This t y pe of interaction, namel y Human -Computer Interac tion, could benefit if the
attention of the scientific rese a rch a nd d evelopment will be more focuse d on the user itself.
The majorit y of present and past research is foc using on methods to achieve best
performa n ce in a HCI system b y p roposing tasks that can tri gger a more powerful effec t in
the human brain, without driving the attention to the user needs and desires. In order to
improve this interaction, the attention sho uld be focused on the user, b y searching for tasks
related to user preferences, or b y fo cusing on us er decisions or on the c urrent user state in
order to control the respective applic ation or an external device.
The ke y concept b ehind thi s enhanced BC I m e ntal state detection technolog y , is the
type o f interaction, also called a s y mbioti c interaction, which de rives from the well-known
natural symbiotic relationship, where two di fferent and conflicting organisms coexist together
in a mutual relation. Ea ch organism benefits fr o m the other and the ant ag onistic condition
that was present before is automaticall y c ancelled out. As an example, the sea anemone and
the clownfish coexist in a mut ual intrinsic relationship b y protect in g from preda tors and
nourishing each other. T he anemone does not strike the clownfish with her sti ngers and the
clownfish does not eat the nutrient tentacles of the anemone. Rather, th e clownfish feeds
it self with the le ftovers from other fishes, cl eans the anemone, and cha s es awa y anemo ne’s
fish predators, like the butterfly fish. In return, the anemone prot ects the clownfish with its
toxic tentacles and rec eiv es nutrients in form of waste from the clownfish. In a similar way, a
HCI should inte g r ate this s y mbiotic relationship b y careful ly inspe cting th e desires and n eeds
of the user a n d fulfilling them, in accordance.
Therefore, the lon g-ter m goal of thi s research is to infer implicit user variables in rea l -
time and silently ad apt the user interface with in t he Brain-Computer Interface o r the Hum an-
Computer Interaction systems. First ly , user ’s interest and needs h ave to be taken int o
consideration; second ly , the current us er ’s state a nd finall y the interface h as to be adaptated
according l y.
The present thesis aims to establish experimental designs that foc us on t he user, in
order to create an impro ved and more n atural hu man -computer interaction. I n this case, th e
potential applications of a s ystem that allows real-time estimation of the current user stat e
include enhanced human-computer interaction, such as information seeking application or
operator assistance s ystems in industrial workplaces (as discussed in Venthur et al., 2010 ).
Furthermore, we envision the use in any control BC I application invol ving healthy
participants.
In view of the targe t sc enario, the goal is to:
 Develop optimized stimulus and tasks to create user-friendly B C I para di gms;

Chapter I. Introduction
4
 Relate to the user state – the BCI should foc us on the user;
 Create inter p retable BCI s y stems;
 Improve the signal processing methods to detect more accurate ly the processes
originating fr om b rain sources or to assist for a better reconstruction of the signals;
 Investigate deeper the information contained in the brain in ord er to i ncrease our
understanding of human brain processes;
 Take a step further towards a user -friendl y BCI in teraction s ystem that can be further
used for control or communication for healthy p eople or people with disabilities
Aiming reducing the complex ity and interaction a t the user side and also crea tin g
interpretable BC I s, the complexit y has to be s witched to the BCI s y stem. The refore, the
objectives of this thesis relate to the developm ent of im proved BCI paradigms, tasks and
scenarios that ease the interaction and to the development of a dvanced and improved
methodologica l approaches for extractin g and interpreting user neural activity from the
electrophysiologica l sig n als recorde d with EEG, while discarding the non-related co rtical
activity. As a reference, the neuroph y siological interpretations will be compared to
behavioral measure ments .
The majority of current BCI s y st ems recognize d iffere nt mental states in rapport with
preliminary training data. In this matter of classification, the g en era l issue of BC I s is that the y
act as an unseen process, being hard for a researcher to ve rify and interpret what the s y stem
actually learns from the data. Recently, various researche rs ex pressed the necessity to
develop appropriate si g n al processing and classif ication techniques for BC I in order to gain
knowledge and insi ghts into the dy namics of the brain and the corre spon ding mental states
(McFarland et al., 2006 ; Kübler and Müller, 2007 ; Müller et al., 2008 ; Blankertz et al., 2010 ;
Blankertz et al., 2011 ; Vidaurr e et al., 2015 ). This approach should become a necessit y fo r a
researcher or de v eloper to correct a BCI to dete ct the corresponding n eura l processes.
Moreover, if the BC I will fa cilitate an as y n chronous interaction (Ma son and Birch,
2000 ), such as when the user can communicate with the int erfa ce at their own will or if the
BCI will silent ly anal y z e user mental state, the human-computer interaction will become
more natura l, efficient and user-friendl y .
Another issue that needs to be addressed relates to t he number of classes ge n era ll y
used in BCI s ystems. Most BCI s y stems are constrained onl y to two classes, being hard for a
user to control a BC I s y stem especially wh en more degrees of fr eedom are requested.
Therefore, a solut ion is clea rl y needed in this as pect b y d esigning algorithms and BC I s that
can e fficiently identif y a greather number of ment al states and tasks (Dornhege et al., 2004a ;
Kronegg e t al., 2007 ; V enthur et al., 2010 ).
1.3 Outline of the thesis
User mental state Brain-Computer I nterfaces will brea k the ic e of the inte rac tion between a
user or patient and an ex ternal machine, if th e signals are s ilentl y recorded and the dec isions
are p erformed in real-time. Aimi ng this lon g-time purpose , w e investigate vi a spe cial
designe d BCI scenarios, the user -related tasks that could brin g us closer to wards this goal. In
this topi c, the thesis evaluates the signal processing and machine learning met hods that could
help investigating and discriminating the brain ac tivit y , while meantime le aning towards the

1.3 Outline of the thesis
5
development of an interpretable BCI. The brain activit y investigated in thi s research work,
was non-invasive recording s via Electroencephalography.
Firstly, the neuroph ysiologica l concepts and basic principles that underlies a BCI
system along with the ke y components of which it is composed, are primarily desc ribed in
Chapter 2 . Further, the signals t ypes investigated i n this thesis that can be used to drive a BCI
system are detailed, n amely th e Event-Relat ed Potentials (ERPs) and the oscillator y
modulations given b y co gnitive activit y and Sensorimotor Rh y thm s (SM Rs ). I n addition, a
standard notation for the mathematica l concepts u sed in this thesis is detailed.
Further, a brie f overvie w of th e existing ana l y sis methods within the scientific
research is presented in Chapter 3 , including the current BCI desi g ns and applications
(Chapter 3.5 ). The d escription of the existing signal p rocessing and machine learnin g
approac h es, focusin g mo re on those that are furth er used in th is thesis, spans from the br ain
activity measurement ( 3.1 ), preprocessing o f the EEG signals along with filtering and
artifac tu al removal techniques ( 3.2 ), and continuing with the feature ex traction and selection
mechanisms ( 3.3 ) towards the classification ( 3.4 ).
To acc omplish the purpose of this thesis described earlier, first user specific tasks
we re investi gated in the well-used t y pe of BCI, namel y mot or-imagery based BCI. The idea,
described in more det ail in C hapter 4 , anal y z es attractive and efficient t asks for the us er in
orde r to attr act user ’s interest and th erefore impr ove the int eraction with the BC I. The brain
responses elicited b y t he internal motor imager y event that generates changes in the
oscillatory activit y , namel y sensori-motor rh y thms (SMRs), are inspe cted considering the
temporal, spatial and spe ctral information of the EEG activity. Specifically , the attenuations
or increases in the alpha rhythm are clo ser investigated b y t he Event-Related
(De)Synchronization (ERD/ERS) phenomena. The respective modulations changes can be
easily obse rv ed b y th e event related brain d ynamic responses in the power spectrum,
therefore by stud y in g the time-frequency representation given b y the Even t Related Spectral
Perturbations measure (ERSP ). Aiming a faster user re action and a stron ger brain response,
two different t y pes o f st imuli, visual and auditory a re evaluated in th e experimental study.
After appropriate prep rocessing that clears the signal from unwanted artifacts and increases
the signal to noise ratio of the sig nal, an enhanced classifie r based on multi -modal features
provides a ver y g ood discrimination of the mot or image r y mental tasks. Further, the section
4.5 describes possi ble futur e developments and optimizations of this stud y . Th e work
comprised in this c hapt er was performed at th e Department of Applied Electronics and
Information Engineering, Facult y of Electroni cs, Telecommunications and I nformation
Technology, University Poli tehnica of Bu charest and was published in fo ur scientific papers
[ 5 , 10 , 11 , 12 ] and a scientific re port [ 13 ].
Further mo re, an innovat ive interface is investigated in Chapter 5 , wh e re the user
mental state is taken into c onsideration, based on the depth of cognitive processing the
external visual information. I mplicit information about the curre nt user ’s cognitive state,
among different levels of cognitive state, could be later use d in a Human Computer
Interaction, for example in an information se eking application or an industrial operator
monitoring setting, with the appropriate machin e learning adaptations for online detection.
The concept of differenti ating between diff erent levels of cognition, could be used to
automatically adapt the application interfa ce by reducing or increasing the amount of

Chapter I. Introduction
6
information presented or the activities requ ested t o be performed b y th e user. This will be a
benefit for the inter action, making it more effici ent, by first ly replacing or supplementing
explicit input with the BCI’s automatic brain state detection , and second ly b y automaticall y
adapting the interface acc ordin g to user’s need s and current state, without the need for
additional setup. The feasibilit y of using BCI i n such new contex ts is investigated in this
work by indu cing different levels of cognitive processing, in ord er to : iden tify and stud y the
corre spondin g neural correlates, investi ga te th e related EEG features , and to adapt the
necessary si gna l processing and machine learn ing techniques. The amount of cognitive
processing is tri ggered by t ask inst ructions in a specific desi gne d experimental paradigm ,
analogous to an odd-ball paradigm with visual stimulus pre sentation, in which the user takes
decisions in accordance to the cor responding t y p e of cognitive pro cess (memory, lan guage
and visual imagination) and th e lev el of processing (no-processing, s hallow and deep
processing). The brain responses investigated here ar e the Event Related Potentials (ERP s )
and modulations of the oscillatory activit y (Event Re lated D e/ Sy nchroni zations), generated
by the cognitive events. C onsidering data analysis, advanc ed signal processing methods were
applied in order to reject non-neural components and keep onl y the brain ac tivit y r elated to
the investigated user ’s state. Different spatial filtering methods are applied in order to reduce
the effect of volume conduction and enhanced fe ature ex traction methods are applied to
detect the optimal neural components. Classifica tion is applied in multi-modal for m, by
integrating the information ex tracted from the sp atial, temporal and oscillatory domains. In
addition, differe nt classification techniques are evaluated for obtainin g best performances.
Firstly, binar y cl assification is evaluated, and then multi-class discrimination is implemente d
to bring the classifi er clo ser to a real application implementation, where the classifier has to
automatically detect betwee n different user states. Next, future developments are pointed out
considering the signal processing and machine learning techniques that could be fu rther
improved and the directions that need to be t aken further when switchin g towards r eal life
applications. The resea rch was performed at the Neurotechnolog y Group, Institute of
Software En gineering and Theore tical Computer Science, F aculty of Electrical Engineering
and Computer S cienc e, Technical Universit y of Berlin and a big p art of the work presented in
this chapter has been publi shed in six scientific papers [ 2 , 3 , 6 , 7 , 8 , 9 ], one in progress [ 1 ] ,
and one database [ 4 ].
In the last chapter (Chapter 6 ), overall con clusions (sec tion 6.1 ) are drawn for the
work in this thesis, referring to the findings and the impact of thi s work on the scientific
research. Further, the fu ture developments and directions that c ould be taken starting from
this research are discussed in section 6.2 .
Supplementary figures a nd description considering sin gle participant graphs, various
electrode s and additional anal y sis are comprised i n the Appendix section.
1.4 Scientific contri bution
The thesis aims to cont ribute to the field of EEG anal y sis b y conside ring advanced and
adapted si gnal processing techniques for the corresponding paradigms and by proposing and
evaluating specific B C I tasks and mechanisms towards mental state d etection that could
improve the brain-computer interaction. After careful evaluation of the ex isting methods and

1.4 Scientific contribution
7
practice s and their short- coming s, spe cific analysis scenarios were selected and proposed that
better detec t the corresponding ne ural activity and user related effective tasks are proposed
that could drive more interest from the use r ’s side and could ease the communication with a
future BCI ap plication. The proposed approache s are evaluated in two experimental studi es
with laborator y settings (Chapter 4 and 5 ) and pu blished in [ 5 , 10 , 11 , 12 , 13 ] and [ 1 , 2 , 3 , 4,
6 , 7 , 8 , 9 ], respectively.
The core parts of this re s earc h are described hereunder:
 Concerning the user interest and his tendenc y o f losing enthusiasm and becoming tired
and disinterested in inter acting with the BCI, spe cific user tasks are firstl y investigated in
a motor-imagery experiment that triggers interest, attention and forces continuous
interaction with the c or responding BCI.
 The second main inter est refers spe cifica ll y to th e user ’s side, b y makin g use of the user ’s
cog nitive state, expressed in the corresponding n eura l correlates , triggered when
cog nitivel y processing the visual external infor ma tion.
 The novel approaches are investigated in experimental studi es on healthy participants, in
order to test the a pplic ability of these concepts to a more realistic scenario.
 For investi gating and extracting the co rresponding neu ral correl ates th at relate to the
corre spondin g a ctivities, powerful signal processing and machine learni ng appro aches
have be en int egrated for the neuroph y siolo g ic al and behavioral dat a an aly sis. Different
data techniques were evaluated and combined to obtain highest performa nc es,
considering mul ti-variate anal y sis, by combining the spatial, temporal, and spectral
domains.
The experimental studies focus on EEG, which is widely used due to its dramatically
lower costs and better p ortability. The anal ysis discussed in this thesis refers primaril y to
EEG analysis, althou g h t he methods ma y be adap ted and tested to other t ype s of acquisition,
but this aspect was not evaluated in this research.
Th e present thesis advan ces the BCI and N eurotechnology research field in various
directions. W hile one one side, r ece nt state -of-the art ma chine le arning techniques can
expand the usabilit y of BCI technolog y, on the other side, the research should focus towards
BCI us ers. Therefore, two innovative BC I p aradigms are proposed in order to inspect the
neural correlates of specific use r tasks or cognit ive user ’s state. I t was shown that such
paradigms could im prove the communication wit h BCI and could make it more p ractical and
accessible compared to earlier approa ches. Th e state- to -the-art anal y si s techniques were
tested for the corresponding investigated neural correlate s and combined when reveal ed
shortcomings. Thus, n ovel approaches are constructed to improve the classific ation
performa n ce of brain data, particularly for EEG .
Shortly, the personal scienti fic contribution s of me, the researcher in quest ion, can be
described by the following main developments:
 Two studies were c arr ie d out, both focusing on the user ’s side for the be nefit of human-
computer interaction. The studies show the applicabilit y of the user related concepts to
future Brain-Computer Interface applications (Section 4.1 and 5.1 )
 The experimental de signs and stimuli presentations necessary for the two studies were
carefully planned and d eveloped (based on the supervisors’ proposals and under their
guidance) in ord er to elicit the desire d brain r esponses. Therefore, th e graphical design of

Chapter I. Introduction
8
the stimuli, the software setup for the presentations and the hardware s etup for brain
signal acquisition we re entirel y performed b y the re searcher in question (sections 4.2.1
and 5.2.1 )
 For the first ti me, the context of levels of cognitive p roce ssin g encountered in
neuroscience and ps ychology are evalu ated in a BCI scenario , in term s of a possible
human-computer interac t ion application (Chapter 5 )
 Specific schemes and investigations for e ffective anal y sis of the neural activit y are
proposed, tested and entirely d eveloped for both studies (Section 4.2.3 and 5.2.3 )
 Specific artifact removal techniques hav e been investigated and applied in order to reduce
head, muscle an d e y e movements that are also p resent in more realistic scenarios ( Section
4.2.3.1 , 5.2.3.2 , 5.2.3.3 )
 Discriminative mea sures are applied in order to investigate in to more de tail the neural
activity (Section 4.3.2 , 5.2.3.4 ). Single-participant representations, as well as grand-
average representations are care full y inv estiga t ed (Section 4.3.3 , 5.3.2 , Appendix A.2.1 ,
A.3.2 , A.3.3 ). Special attention is given to the trial- by - trail and among participants’
variability of the EEG data. Different feature extraction methods are im plemented a nd
optimized in order to extract the most relevant bra i n activity (Section 5.2.3.4 ).
 In addition, careful ve rification of the si g nals has been performed in order to assure that
the extracted components which will be given to the classifier, hi ghly reflect the cortical
activity a nd not some artifacts (Sec tion 4. 3. 3.3 , 5.2.2.3 )
 An improved ensemble classification approach is developed based on multi -modal
analy sis, t aken advantage b y the t emporal, spatial and the spectral characteristi cs of EEG,
in order to overcome the limitation of single domain ana l y sis (Section 4.2.3.2 and 5.2.3.5 )
 Appropriate multi -classification approach is implemented, necessar y for further online
classification (Sec t ion 4.3.4 and 5.3.3.2 )
 New ne uroph ysiologica l findings related to different levels of cognitive processing are
detected and investigated (Section 5.3.2 )
 Corresponding Matlab code was developed for each pro cessing scenario and some
adaptations to existing methods in orde r fit the corresponding proce ssi ng pipelines are
public available in the BBCI Toolbox (for example: ssd -bank ) (Section A.1.4 , A.2.2 )
 Scientific research pap ers and articles that describe the corresponding approaches are
published, further de s cribed in Section 1.5 [ 1 - 13 ].
1.5 List of contributions
The contributions performed on the research described in this thesis have been
published in peer-reviewed journals and con ference pro ceeding s. The following section 1.5.1
list those articles, in ch ronological ordered starting with the most recent ones. The next
Section 1.5.2 , refers to all the additional peer-r eviewe d publications which were not included
in the thesis, that I have authored or co-authored since the be ginning of the PhD (O ctober
2013) in the domain of signal proc essing. Specificall y , the y include additional research on
Electroencepha lo graphy (EEG), El ectrocardiograph y ( ECG), Electrooculography (EOG),
Electrohysterography (EHG), and image processing. A complete li st of all the publications

1.5 List of contributions
9
including other domain s such as s y nth etic biology or the manuscripts published before the
beg innin g of the PhD (October 2013), can be found online on the following scientific
research w ebsites : Researchga t e or Academia . C onsidering the number of citations, the h-
index was specified by Google Scholar on the 1th of March 2018 for a ll public ations that
have been published for at least 6 months and it r epresents the level h-3 which indicates that
at least 3 manuscripts have been cited at lea st 3 times.
1.5.1 M ai n c ontributions list
Publications in preparation or under review
1. I. E. Nicolae , B. Blankertz, Offl ine cognit ive mental stat e detection: multi-classification
approach for the use in BCI applications , 2018, I EEE indexed, I SI. (in preparation)
Published publications
Journal papers
2. I. E. Nicolae , G. M. Neagu (Ungureanu), R. Strungaru, L. Acquala gna , B. Blankertz
( 2018 ) Enhanced classification for the depth of cognitive proc essing depicted in neural
signals , UPB Scientific Bulletin , S eries C, Vol. 80, I ss. 1, pp. 135-146, I SSN 2286-3540,
WOS:000428622400012, ISI indexed.
3. I-E Nicolae , Acqualagna L and Blankertz B. ( 2017a ). “Assessing the Depth of Cognitive
Processing as the Basis for Potential User- State Adaptation”. Front. Neurosci. 11:548, 2017a ,
WOS:000412319200001, doi: https://doi.org/10.3389/fnins.2017.00548 , I S I indexed.
Conference pape rs , book chapters, abstracts an d data se ts
4. Nicolae I-E , Acqualag na L and Blankertz B. ( 2017b ). Assessing the De pth of Cognitive
Processing as the Basis for P otential User -State Adaptation – Data set. Depositonce,
Technische Universität Berlin. doi: https://doi.org/10.14279/depositonce-6173
5. I. E. Nicolae , M. M. C. Ștefan, B. Hurezeanu, D. D. Tar alunga, R. Strungaru, T. M.
Vasile, O. A. Bajenaru, G. M. Ungureanu ( 201 6b ) Investigating Motor Imagery Tasks by
Their Neural Effects – a Case Study , P roc. of the 38 th Ann ual Intern. Conf . of the IEEE
Engineering in Me dicine and B iology Society , 17-20 August, Orl ando, Florida, pp. 5861 -
5864, WOS: 000399823506052, doi: https:/ /doi.org /10.1109/EMBC.2016.7592061 , I EEE
Xplore indexed, I S I.
6. I. E. Nicolae , L. A cqualagna , B. Blankertz ( 2016a ) Investigati ng Depth of Cognitive
Processing in the Brain Dynamics of Os cillations , Proc. of th e Sixt h Intern. Brain -
Computer Interface Meeting , Ma y 30 - J une 3, California, USA, p. 186, Published by
Verlag der TU Graz, Graz University of Te chnology , ISBN 978-3- 85125-467-9, doi :
https://doi.org/10.3217/978-3-85125-467-9-186 .
7. I. E. Nicolae , L. Acquala g na, and B. Blankertz ( 2015b ) Tapping Neural Correlates of the
Depth of Cognitive processing for Improving Human Computer Interaction , 4th I ntern.
Workshop on S y mbioti c Interac tion, L ecture No tes in Computer Science, Springer , Vol.
9359, pp. 126-131, Print I S BN 978 -3-319-24916-2, doi : https:/ /doi.org/10.1007/978-3-319-
24917-9_13 , BDI (Scopus) indexed.

Chapter I. Introduction
10
8. I. E. Nicolae , M. U ngurea nu, L. Ac qu alagna, R. Strunga ru and B. Blankertz ( 2015c )
Spectral Perturbations reflect the Depth of Cognitive Processing for Brain -Computer
Interface S ystems , Proc. of the 5-th Intern. Conf . on e-Health and Bioengin eer ing , 19-21
November 2015, Iasi, Romania, pp. 1 -4, WOS:000380397900126, doi :
https://doi.org/10.1109/EHB.2015.7391473 , IEEE Xplore indexed, ISI.
9. I. E. Nicolae , L. Acqualagna, and B. Blankertz ( 2015a ) Neural Indicat ors of the Depth of
Cognitive Processing for Use r -Adaptive Neurotechnological Applications , Pr oc. Of the
37th Annual International Confere nce of the IE EE E nginee ring in Medicine and
Biology Society , August, Milano, I tal y , pp. 1484 -1487, WOS:000371717201190, doi:
https://doi.org/10.1109/EMBC.2015.7318651 , IEEE Xplore indexed, ISI.
10. I.E. Nicolae , G. M. Ungureanu and R. Strungaru ( 2014a ) ICA Analysis of Real and
Motor Imagery Movements in a Brain -Computer Interface Stimuli System , Proc. of the xth
Intern. Conf. of Electronics, Com puters and Artificial In telligence , 23-25 oct, 2014,
Vol. 6 – No. 6, 4 pages, WOS:000380489500047, doi :
https://doi.org/10.1109/ECAI .2014.7090216 , I EE E Xplore indexed, I S I.
11. I.E. Nicolae , G. M. Ung u rea nu, and R. S trung aru ( 2014b ) Motor Imagery Mental Tasks
in Brain-Computer Inter face Applications , Workshop on S m art Healthcare and Healing
Environments , AMI'14, 11-13 nov, Eindhoven, The Netherlands, 12 pages.
12. I. E . Nicolae ( 2013 ) An improv ed stimuli system for brai n-computer interface
applications , Proc. of the 5 th Intern. Conf . on Electronics, Computers and Artificial
Intelligence , 27 -29 J une, Pitesti, Romania, Vol. 3, pp. 49 – 53, W OS:000343672500006 ,
doi: https://doi.org/10.1109/ECAI .2013.6636157 (Extended version published also in:
Intern. Journ. of Monitoring and Surveillance Te chnologies Research , Special Issu e on
Biomedical Monitoring Tec hnologies, 1(4), 1-8, Oct-D ec 2013 , I NSPE C:14907402, doi :
https://doi.org/10.4018/ijmstr.2013100101 ), IEEE Xplore indexed, I S I.
13. I. E . Nicolae , “ Extracting signal from n oise in Brain - Machine Interfaces” , P hD
Scientific Report, June 2014.
1.5.2 A dditional contributions list
Publications in preparation or under review
14. I. E. Nicolae , M. Wenzel, B. Blankertz, et al., Studying the neural correlates of high
versus low focus user states for BCI applications , 2018, IEEE indexed, I S I. (in preparation)
Published publications
Conference pape rs
15. R. Al. Cernat, A. M. Speriatu, D. D. Taralung a, B. E. Hurezeanu, I. E. Nicolae , R.
Strungaru, G. M. Un g u reanu (2017) Stress Influ ence on Drivers Identified by Monitoring
Galvanic Skin Resistance and Heart Rate Variability , Proc. of the 6 th IEEE In tern. Conf .
on E-Health an d Bioengineering (EHB) , J une 22 -24, S inaia, Roma nia, pp. 261-264,
INSPEC:17066086, doi : htt ps://doi.org /10.1109/EHB.2017.7995411 , IEEE Xplore, BDI
(Scopus) indexed.

1.5 List of contributions
11
16. B. Hurezeanu, G. M. Ungureanu, D. Taral unga , I. E. Nicolae , W . Wolf, R. Strungaru
(2016) Real-time eye movement detection analysis: Comparison between image based
algorithm and electrooculogram , P roc. of the 9th Intern. Conf. a nd Exposition on
Electrical and Power Engin eer ing (EP E) , 20 oct 2016, I asi, Romania, pp. 372 -375,
WOS:000390706300075, doi : https://doi.org/10.1109/I CEPE.2016.77813 65 , I EEE Xplore
indexed, ISI.
17. D. D. Taralunga, G.M. Ung ureanu, B. Hu rezeanu, I. E . Nicolae , R. S trung aru (2016)
Non-contact heart rate estimation from a video se quence , Proc. of the 9th Intern. Conf . and
Exposition on Electrical and Power Engin eering (EPE) , 20 oct, I asi, Romania, pp. 348 –
351, WOS: 0003907063 00070, doi : https://doi.org /10.1109/ ICEPE.2016.7781360 , I EEE
Xplore indexed, I S I.
18. M. M. C . Ștefan, I. E . Nicolae , R. Strungaru, M. G. Ungureanu , M. Vasile; O. A.
Băjenaru (2016 ) A study on the efficient tracking of ERD based on the adaptive identification
of the subject's reactive band , Proc. of the 9th I ntern. Conf . and Exp os ition on Electrical
and Power Engineering (EPE) , 20 oct, I asi, R omania, pp . 337 – 343,
WOS:000390706300068, doi : https://doi.org/10.1109/I CEPE.2016.77813 58 , I EEE Xplore
indexed, ISI.
19. M.M.C. Stefan, I. E. Nicolae , R. Strungaru, T. M. Vasile, O. A. Băjenaru , G. M.
Ungurea nu (2016) Adaptive Modelling of EEG Si gnals to Produce Accurate T ime-Frequency
Decompositions for Use in BCI , Proc. of the 8th In tern. Conf . of Electronics, Computers
and Artificial In telligen ce , 30 June - 02 Jul y , P loiesti, R omania, 4 pa ges, vol 8, no. 2/2016 ,
WOS:000402541200024, doi: https://doi.org/10.1109/ECAI.2016.7861088 , IEEE Xplor e
indexed, ISI.
20. V. Straticiuc, I. E. Nicolae , R. Strungaru, T. M. V asile, O. A. Băjenaru , G. M .
Ungurea nu (2016) A preliminar y study on the e ffe cts of music on human brainwaves , Proc. of
the 8th Intern. Conf . of Electronics, Computers and Artificial In tellig ence , 30 J une - 02
Jul y , P loiesti, Romania, 4 pages, vol 8, no. 2/2016, W OS:000402541200132, I NSPEC:
16692514, doi: https://doi.org/10.1109/ECAI.2016.7861196 , IEEE X plore index ed, I S I
indexed.
21. B. Hur ezeanu, I. E. Nicolae , D. Ta ralunga, Rodica Strung a ru, W. Wolf, I . Gussi, M.
Ungureanu (2015) Fetal monitoring: identifying the relevant biosignals interdepende nci es ,
Pr o c. of the 5-th Intern. Conf. on e-Health and Bioengin eer ing , 19- 21 November , Iasi,
Romania, 4 page s, WOS:000380397900098, doi : htt ps://doi.org/10.1109/EHB .2015.7391445
, I S I indexed.
22. R . Cojocaru, D. Popescu, I. E . Nicolae (2013) Texture Classif ication based on
Succolarity , Proc. of the 21 st Telecommunications f orum , 26-28 November, Belgrade,
Serbia, section 5, pp. 49 8-501, WOS:000349857500113, doi :
https://doi.org/10.1109/TELFOR.2013.6716275 , IEEE Xplore indexed, I S I.

Chapter I. Introduction
12

13

Chapter 2

Fundame ntals of neurophysiolog y

Foreword

When we thi nk about the brain, we can think of the giant unive rse spre ad across bil lions of
light-years, with conste llations of information having billions of conne ctions between them.
Only size makes a difference with billions of light-years for the cosmos and onl y micromet ers
for the b rain. Glancin g through the microscope, th e brain contains around 100 billion neurons
with 100 trillion conne ctions (s y napses), which mean 700 billion times less than the
estimated number of sta rs in the obse rvable universe (The Australian National University ,
2003 ). Howsoever, this ti ny b rain can „ contemplate even the vastness of interstellar space ”
(Ramacha nd ran, 2009 ). Now, as you probabl y vi sualized, we can ba rely scratch the surface
of thi s huge amount of information. As telescopes investi gates pl anets activit y , so
Electroencepha lo graphy (EEG) records th e brain signals, getting closer to touch the unseen .
By this means, y ou wil l fee l like y ou almost hold petab y t es of brain informatio n with
thousands of thoughts, memories and knowledge in the palm of your hand.
Now, how we can grab information from the brain, it will be described in this chapter.
Starting from the theoretical aspects of the human brain and continuing with the measures
and methods that helps decipher and better understand the neural information.
2.1 Neurophysiological background
The neural ac tivit y generates changes of electrical fields (Buzsáki et al., 2012 ). The ionic
curre nt produced by t he neurons within the brain that generates action potentials
(postsyna ptic pot entials – PSPs , given b y d epolarizations of the neuronal membrane),
propagates through the cortex until it reaches the surface of the scalp ( Niederme y er and Silva,
2005 ; Buzsáki et al., 2012 ). For this reason, the electrical a ctivity arrived at the surfac e o f the
scalp will be weaker than the original activit y , from millivolts ( mV ) down to mi crovolts ( µV ).
The original electrical potentials from one source of the brain propagate dif fere ntl y within the

Chapter II. Fundamentals of neurophy siolo gy
14
cortex due to different electrical conductivit y o f t he brain and therefore, o n the surface of the
scalp the electrical potenti al could arrive at a dist ance from the original region of the b rain.
The effec t is known as volume conduction (Rutkove, 2007 ).
2.2 Electroencephalography
The first attempt to record electroph y siological brain signals dating from 1875 belongs to
Richard C aton who pr esented his findings about the electrical ph enomena of cere br al
hemispheres of animals (Caton, 1875 ). Later on, in 1924, after ex tensive experiments, Hans
Berger (Berger, 1929 ), a German neu rologist, discovered that besides animals, also the
activity of the human brain can be gripped b y placing an electrode on the surface of the scalp.
Next, the signal is amplified and visualized as changes in volt ag e over time. Th is technique
of brain si g n als recording (Niederme y er a nd Sil va, 2005 ) will be later called
Electroencepha lo graph y (EEG). Over the decades, EEG proved to be very useful in scientific
research and clinical app lications, mainly due to its high temporal r esolutions, not found b y
the other hemod y namic measures, such as: positr on emission tomography - PET, functional
near- inf rared spectros copy - fN I RS, functional magnetic r esonance imaging - fMRI .
Moreover, it is wide l y used due to its affordability, portabilit y and non-invasive
character istics. Althou gh, there is o ne drawback of EEG, namely low spatial resolution. T o
put it sim p l y , EEG is similar to a symphon y , composed b y a complex mixture of sounds that
change in time and space with varying ph ase, pitch, tone, volume (amplitude) and f requency.
In EEG, there is mixt ure of brain signals coming f rom different sources of neura l activit y due
to the volume conduction effect des cribed earlier . However, this effect can be reduced to
some extend based on advanced source separation techniques.
Apart from EEG, other b rain measurement techniques may be used (briefl y desc ribed
later in Section 3.1 ), which do not constitute the purpose of this work.
2.3 Bra in -Computer Interface
After almost 50 y e ars from the discovering of h uman brain sign als, a ne w t y pe o f int erface
dramatically d eveloped, namel y B r ain-Computer I nterface (B C I). The sy stem unifies the
interaction between a brain and a computer b y me asuring the neu ral activit y and translating it
into an action that could also be extended to an external device (Dornhege et al ., 2007 ). The
first rec o rds of the name BCI state from Jacques Vidal (Vidal, 1973 ) who first relied on
visual evoked potentials to perform screen cursor control. As W olpaw and Wolpaw ( 2012 )
describes, a BCI s y stem aims to replace, r estore, enhance, supplement or im prove human
sensor y -motor or cognitive brain functions.
A re al-time BCI s y stem requires two main phases to be fulfilled: an offline training
used to calibrate the s y st em and an onl ine phase where the BCI detects and interprets user’ s
brain activity or user’s current mental state and translates it in real-time into a command for a
computer, which can be t ransferred to an external device. The online part of the BCI r efers to
a closed-loop process a n d is usually composed of six steps: neural activity recording, pre-
processing, f eature sel ection a nd extraction, c l assification, tra nslation into a command and

2.4 Neurophysiolog i cal signals that enable BCI control
15
providing feedback for the user (Mason and Birch, 2003 ). When the feedback is not provided,
the BCI is considered a n open-loop BCI. The se steps are described in the following:
1) Neural activity recording : consists in rec ording the brain signals o f a user reflect ing
the brain activity, b y usi ng different types of sensors or measure ment tec hniques (W olpaw et
al ., 2006 ). This signa ls ac quisition step usuall y involves also a preliminary on -line cleanin g
of the signals to reduce the noise caused b y elec tronic devices, cables, et c. In this research,
we focus on the scalp electric al potential measu rement technology, namely EEG.
2) Pre-processing : performs detailed cleaning and denoising of the brain data in order to
improve the quality of the signals and increase the detection of relevant information
incorporated in the si gnals (Ba sh ashati et al., 2007 ).
3) Feature extraction and selection : detec ts the most relevant characteristics of the
signals, named fea tu res that best describe the brain activity (Bashashati et al., 2007 ).
4) Classification : discriminates the group of features detected from the brain data by
assigne d them to a correspondi ng cl ass, referrin g to the t y pe of brain activity or mental state
identified (Lotte et al., 2007a ).
5) Translation into a command to a computer ap plication or to an external device : After
the specific mental stat e is identified, a command is associated to this bra in a ctivit y or
corre spondin g mental sta te that controls the given application. An ex ample is an avatar in a
virtual reality environment (Léc u yer et al., 2008 ), or a robot or prosthesis (Kübler et al.,
2006 ).
6) Feedback/Neurofeed back/Biofeedback : the la st and the most important step provides
information (visual, auditory, tactile) to the user about the BCI decision ( it can also be the
actual output of the BCI application, e.g. the movement of a robotic arm). This closes the BCI
loop a nd is use ful for the user in order to control his brain activity and therefore the B C I
(Wolpaw et al., 2002 ) . The feedback is mostl y given to the user i n form of visual
repre s entations of the changes in the ongoing EEG (Neuper and Pfurtscheller, 2009 ),
although it can comprise also auditor y (H interberger et al., 2004 ; Hwang et al., 2009 ) or
tactile (Chatterjee et al., 2007 ) feedback.

In this re se arc h, we re fer to the offli ne part of the BC I , with the purpose of
preliminary det ecting th e corresponding b rain features for silentl y inve stigating the user ’s
brain activity and mental state in a future feasible BCI application. This off line BCI consist in
the following steps: measurement, pre-pro cessing, feature ex traction and classification, with
no real-time fee dback given to the user. Here, also a behaviora l measure feedback is showed
to the user, related to its performanc e, but is important not to confuse with the actual
feedback of a BCI s y ste m as des cribed above . Further in thi s thesis, when we refer to the BC I
system, we m ean the off-line BCI s y stem.
2.4 Neurophysiological s ignals that enable BCI c on trol
Different types of br ain potentials have been st udied b y the BCI communit y and most of
them are relatively e asy t o be identified b y a computer, but a bit more cumbersome for a user
to control his own potentials. Based on the t y pe of the signal generator, t wo t y pes of signals
are inv estigated (Wolpaw e t al., 2002 ; Curran and Stokes, 2003 ) : Eve nt -Related P otentials

Chapter II. Fundamentals of neurophy siolo gy
16
(ERPs) as well as spontaneous brain signals. The Event-Relate d Potentials are unconsciousl y
ge n erated b y the human brain when the us er perceives an external sti mulus, as a result of a
sensor y , cognitive or m otor event. Here, we remind the common ly use d potential, usually
known as the P300 potential. The main advanta g e of ERPs, in c omparis on to spontaneous
signals, is that the y do not require user training, bec ause th e y are naturaly produ ced b y th e
brain as response to a stimulus. This advantage makes it easier for th e u ser to control a BCI
system (Wolpaw et al., 2002 ; Curran and S tokes, 2003 ). Nevertheless, th ey require constant
focus and repetitious stimuli, which can becom e exhausting and uncomfortable for the user.
On the opposite, spont aneous signals are voluntar ily generated b y the us er , following
an internal motor or cognitive process, without be ing trigge r ed b y an external stimulation.
The widel y used spontaneous signals are the sens orimotor rh y thm s (SMR s) and l ess used are
the non-motor cognitive sig n als. Compared to the ERPs, the s ensorimotor rh ythms can be
voluntarily controlled in amplitude b y the user after intensive training (Wolpaw et al., 1991 ;
Wolpaw and McFarland, 2004 ; Vaughan et al., 20 06 ; Wolpaw et al., 2007 ). The spontaneous
signals a re observed as modulations of spontaneous brain rhythms, nam ely Event-Related
Des y nchronisation (ERD) in case of a decrease in spectral power of the corresponding
freque n c y band (Pfurtscheller and Aranib ar, 19 77 ) or an Event-Related S y nchroniz ation
(ERS) when an increa s e in power appears (Pfurtscheller and Silva, 1999 ; Lemm et al., 2009 ).
2.4.1 Event Re lated Pote ntials
As Donchin et al. ( 1973 ) describe, Event-Related Potentials, ERP (Vaug han, 1969 ) are spikes
in the signa l volta g e caused by the occ u rrence of rare events. Triggered by an e x oge nous
event, the ERP s si gnify the presence of cognitive proce ssin g the external i nformation throu g h
our sensor y s ystems, e.g . visual, auditor y , t actile, etc. I n the present time, the y are usuall y
investigated as a response to a sequence of stimul i divided in target and n on-target, provided
by a BCI, calle d an ‘odd - ball’ paradigm. This potential consists of a succession of positive
deviations in amplitude (e.g. P100, P300) and negative deflections (e.g. N200 , N400). In the
case of a rare event (target), it will elicit a hig her P300 potential (Handy , 2004 ; Luck, 2005 )
about 300 - 500 ms after the sti mulus onset, compar ed to a lower peak in case of a frequent,
un-attentive stimulus (non-target). The ERP shows hug e variabilit y be tween participants
probably due to different folding of the cortex which influences the p ropagation of the si gnal
through the scalp (Luck, 2005 ). Variabilit y is observed also regardin g one participant on
differe nt moments of the da y s, different participant state, or the level of tiredness (Polich and
Kok, 1995 ). How ever, t he an aly sis of ERP has different a dvanta ge s: first compared to the
behavioral measures it provides more in formation about the variations o f a specific cognitive
process, rather than the reaction time and accu racy, for example. S econd ly , it provid es a
continuous measure of processing between a stimulus and a response , making it possi ble to
determine which sta ge of processing is enlightened b y a sp ecific stimul us. Another advanta ge
of ERP is that it can provide an on-line measure of the processing of sti muli (covert
attention), even when there is no behavioral response. In addition, the user requires no
training in order to use the s y stem, because the effect is automaticall y genera ted in r esponse
to an external stimul us. A disadvantage is that it re quires continuous attention and multiple
repeti ti ons of the stimuli.

2.4 Neurophysiolog i cal signals that enable BCI control
17
Cognitive activity
The ERPs earlier desc ribed, also represent different components which are associated
with severa l co gnitive proce sses (Re ga n, 1989 ; L uck, 2014 ). In addition, the cognitive
activities investigated in the oddball paradi gms modify the amplitude and l atency o f the ERPs
in relation to task diff icult y (Ullsperger et a l ., 1987 ; P olich, 2007 , Kim et al., 2008 ). The
effect is observed b y a n increased P300 and longer lat encies, in fluenced b y c omplex
processes and stronger attentional demand. This cognitive potential is characterized b y a peak
ar ound 300 - 500 ms after the stimuli in the centro-parietal cortex (Polich, 2007 ).
2.4.2 Osc illatory brain ac tivity
Complementary to the o bserved effects o f the neural ac tivit y over time, oscillations in the
spectrum g iven by the Event Related De/Synchronization (E RD/ERS ) of the Sensorimotor
Rhy t hm (SMR) provides also ne cessary info rmation of the neural processes. These
electrophysiologica l rh y t hmic activities ( Buzsáki, 2006 ), are generated b y t he firing of groups
of neurons in diff erent frequency bands. A change in the mental state of a health y hominid
ge n erates a change in a fre quenc y band over the entire brain. The corresponding frequen cy
ranges var y betw een pa rticipants, due to diffe rent ana tomical structure. Additi onally , the
location of the brain activity sources provides information about the corresponding brain
function involved. In conti nuation, a de sc ription of the fre qu ency bands (G roppe, 2013 ) a lon g
with the functional behavior that is associated to, are detailed here und er.
Brain waves
 Delta rhythm ( δ, 0.5-4 Hz of 20 to 1 00μV ): The functional desc ription of the d elta
freque n c y band relates to a sleep stage, mostl y for adults. (Armitage, 1995 )
 Theta rhyth m (θ, 4 -8 Hz with >10 μV ): Th eta signifies drowsiness or arousal in
teenagers and adults. In addition, changes in ampl itude related to cognition and workload
have also been observed (Gundel and Wilson, 1992 ; Gevins et al., 1997 ; Klimesch, 1999 ).
 Alpha rhythm (α, 8 - 13 Hz of 20 - 1 00μV) : T he alpha frequency ba n d is mostly
ge n erated b y visual activit y originating from the occipital cortex (e . g. Vanni et al., 1997 ).
Alpha wave s could refer also to attention in the frontal area (Niederme y er, 1997 ) or a relaxed
state (Hughes and C runelli, 2005 ). Opening and closing the e y elids also activates or suppress
in the genera tion of the alpha frequenc y band. In general, experimenters take advantage of
this effec t in ord er to verify the qu ality o f the EEG signals.
 Mu rhythm ( μ, 8-10 Hz ): A special case of oscillations which involves the same
freque n c y r ange as the alpha band (Feshchenko, 2001 ) is represented by th e mu rh ythm,
except that is g enerated by a motor or sensorimotor event which modulates the rh ythm
amplitude (Wolpaw and Wolpaw, 2012 ). More exactly suppression in the μ -rhythm (8-10Hz)
is encountered in the motor cortex regions and is generated b y mot or or motor -imager y
activities (Pfurtsche ller and Silva, 1999 ).
 Beta rhythm (β, 1 3-30 Hz w ith 5 -3 0μV) : A higher frequenc y , named as beta band,
relates to more active processes like motor activity (Pfurtscheller and Silva, 1999 ;
Pfurtscheller and Neuper, 2001 ), conc ent ration, a ctively thinking (mental effort) ( Lacha ux et
al., 2005 ).

Chapter II. Fundamentals of neurophy siolo gy
18
 Gamm a rhyth m (γ, 30 -100 Hz , <10μV ) : The gamma frequency b and appears in
higher cognitive functions, e.g. in learning pro cess es , or motor functions (Niederme y er and
Silva, 2005 ).
While most of the EEG rese arch focuses on the spectrum anal y sis from 1 Hz to 50Hz
(Cohen, 2014 ), the EEG comprises prominent relevant information also in higher f requency
ranges (Curio, 1999 ; Gotz et al., 2009 ; Scheer et al., 2009 ; Nikulin et al., 2011 ; Telenczuk et
al., 2011 ; Buzsáki and Silva, 2012 ; Fedele, 2014 ).
 Note: The fre qu ency bands intervals are not fixed a nd mig ht slightly ex cee d the
specified ranges, e sp ecially between individuals (see below).
Variability between and within participants
When aiming the deve lopment of a general functional online BCI s y st em, it is important to
consider the variabilit y b etwee n and within participants . Variability in the temporal, spatial or
spectral distribution of th e neural information, termed non-stationarit y is o ften encountered in
the EEG. Changes can be generated b y a s eries of factors su ch as: anatomical and biological
(e.g. age, neurolo g ic al diseases, brain structure), non-related neural signals ( e.g. muscle
artifac ts or oth er ph ysiological a rtifacts, mental state, mood, tiredness), technica l (e.g.
electrode conductivit y or electrode position changes), task-related changes (e.g. diff erent t ask
involvement, memory performance), and so on. Ex plicitly , chan ge s i n amplitude, spectra l
power and spatial patte rns between p articipants arise, that can drasticall y in fluence the
performa n ce of a SMR BC I s y stem ( Blankertz et al., 2010a ). Moreover, the variabilit y is
encountered as well wit hin participants (D ähne et al., 2011 ). Several s tudies show these
variabilities while tr y in g to solve this issue ( L emm et al., 2005 ; Blankertz et al., 2008a ;
Sannelli et al., 2011 ; Vidaurr e et al., 2011a , c ; Christensen et al., 2012 ; S amek et al., 2012 ,
2014 ; Dähne et al., 201 4a , b ). Two approac h es are identified that aim a robust BCI: ( 1 )
detecting signal representations invariant to nonstationar ities (Büna u et al., 2009 ) or ( 2)
detecting and alleviating them (Kohlmorgen and Le mm, 2001 ; Schlögl et al., 2010 ; Vidaurre
and Blankertz, 2010 ; Bly the et al ., 2011 ; Vidaurre et al., 2011b ; ). The second suit able
solution invariant to fluc tuations that can increase the p erformance of a BCI s ystem
(Blankertz et al., 2002 , 2008a ; Mü ller et al., 2004 , 2008 ) considers a pa rticipant-depe nd ent
classifier bas ed on a preliminary BC I t raining, in which the individual frequenc y ran ges ar e
detected and the corresponding feature s are s et to the classifier.
2.4.2.1 Modulations of spontaneous brain rhythms
The decrease in amplitude of spontaneous brain rh y thms is well-known as Event
Related-De s ynchronization (ERD) (Pfurtscheller and Aranibar, 1977 ). In general, the ERD is
followed by an increase in amplitude called Event -Related S ynchronization (ERS)
(Pfurtscheller and Silva, 1999 ; L emm et al., 2009 ). These modulations of the amplitude (hull
curves) can span in the alpha and beta bands (Pf urtscheller and Klimesch, 1992 ; Nikulin et
al., 2007 ) and can be ti me-locked to the ex ternal stimul i or trigg ered b y internal functions
(such as voluntary move ments, or cognitive proce sses). Uncommon activities tend to produce
higher modulations than frequent activities , effect known and demonstrated in man y studies
within the scientific communit y . For example, left hand movement execution for right-
handed individuals, pr oduces increased activity as compared to the usual right-hand

2.4 Neurophysiolog i cal signals that enable BCI control
19
execution (K löppel et al., 2007 ), or the observ ation of uncommon acti vities for infancy
produces stronger motor activation, represented by a pronounced des ynchronization in the mu
freque n c y band, as compared to ordinar y actions. (Stapel, 2010 ).
As applications, these EEG band power modulation s are used to control electronic
devices, either via motor imagery or c o g nitive pro cesses (Section 3.5 ).
Non-motor cognitive activity
Besides the use of motor imagery tasks, cognitive processes are also used to drive a BCI
system, for instance: memory, language, visual imagination, ment al numerical calculations,
mental ima ge r y rotation of geometric shap es, et c. (Keirn and Aunon, 199 0 ; Anderson et al.,
1998 ; Millán et al., 2000 ; Curran and Stokes , 2003 ). Each of them g enerate specific
variations in the respective band power and cortical r egions, such as: enh ance d ERD in th e
alpha band for memor y processes (M ecklinger et al., 1992 ; Klim esch et al., 1994 ; Klimesch,
1999 ; Stipacek, 2003 ; J ensen and Col gin., 2007 ; Pesonen et al., 2007 ), or in the processing
and production of words (Klimesch et al., 1997 ).
On the other side, the co gnitive perception of external stimuli produces in particularly
a short ERS followed b y a sustained ERD arisi ng in the α band (Klimesch et al., 1992 ;
Klimesch et al., 1993 ), after the P300 potential ( Yordanova et al., 2001 ). The alpha b and is
known to desync h ronize concurrentl y with mental activit y , namel y to de cre as e in amplitude
with cognitive difficult y, visi ble in the centro-parietal area (Gevins et al., 1997 ; Venthur et
al ., 2010 ). For mor e complex cognitive activities, oscillations in the beta band a re also
encountered (Peson en et al., 2007 ; Okazaki et al., 2008 ; S heth et al., 2009 ), as
desy n chronizations for complex reasoning (Basile et al., 2013 ), dec ision making (Nakata et
al., 2013 ) or in the tr ansition between different cognitive states (Sheth et al., 2009 ). On the
opposite, sy nchronizations are observe d in the theta band ac cording to task difficulty
(Klimesch, 1999 ), for ex ample in higher memory load (Gundel and Wilson, 1992 ; Gevins et
al., 1997 ) or the encoding of new information.
In addition, cog nitive phenomena, have been also shown to corre lat e with band -power
modulations (Varela e t al., 2001 ; Buzsáki and Dr a g uhn, 2004 ), for example: perceptual
encoding and atte ntional process (Sergeant e t al., 1987 ; Ba ş ar et a l., 1997 ; Debener et al.,
2003 ; Klimesc h, 2003 ; Schack e t al., 2005 ; Bauer et al., 2006 ; Polich, 2007 ), vigilance in
operational environments (Gevins et al., 1995 ; Holm et al., 2009 ), perce pt ion (Plourde et al.,
1991 ; Makeig and J ung, 1996 ; Thut et al., 2006 ) a nd decision making (Haegens et al.,
2011a , b ).
2.4.2.2 Sensorimotor rhythms
When motor or motor-image r y actions are intended and e x ecuted, for ex ample the real or
im ag ined movement of a bod y part (Pfurtscheller and Neuper, 2001 ; P furtscheller et al.,
2006 ), the motor cortex is cha racterized b y an o scillatory idl e rh ythm, called sensorimotor
rhy thm (SMR), in the µ (≈ 8 -13 Hz) and β (≈ 13-30 Hz) frequenc y bands . Specificall y , the
movements of the uppe r bod y parts, su ch as hands, generate a d ecrease in power called Event
Related Des y n chronisation (ERD), in the μ rhythm, over the contra-lateral motor cortex area
(the opposite hemisphere ) and an incr ease in band power called Event Re lated
Synchronisa tion (ERS ) i n the ipsi -lateral h emisphere (Pfurtscheller and Sil va, 1999 ). For the

Chapter II. Fundamentals of neurophy siolo gy
20
inferior bod y p arts, for example feet, the oscillati ons are obs erve d in the central mot or cortex.
As for the beta fre quenc y (Pfurtscheller et a l ., 2005 ), the SMR ’s amplitude shows a
de/syc h ronization during motor ex ecution which precedes a short s ync hronization that
appears after the movement, termed as the 'beta rebound'.
Comparing the t y pe o f movement: real o r imager y execution, man y similarities a re
encountered starting wit h resembling ERDs (M cFarla nd et al., 2000 ; Neuper et al., 2005 ) in
the contralate r al sit e according to the respective movement (left or rig ht) and ERSs present in
the ipsilateral cor tex modulated in the mu and beta bands.
The drawback of using S MRs for driving a BCI is that the control commands options
are constrained b y the number of bod y parts. Mo reove r, a subject which h as lost a body part
in an accident, mi ght find it hard to ima gine its movement, firstl y due to the emotio nal
connection and secondl y, due to the la ck of movement execution for a lo ng period of time.
Also, when aiming p rosthesis control, the s y stem ’s decision is constrained by th e amount of
body parts, which will in volve a hi gher complexity to the us er side w hen performing multiple
body parts movements. Generall y , in a BCI based on sensorimotor rh y th ms, the user has to
modify the amplitude of his SMRs for the purpose of controlling the BCI s y stem (Wolpaw
and McFarland, 2004 ; W olpaw et al., 2007 ). Based on this fact, not all users are capable of
controlling the sy stem (Guger et al., 2003 ; Blankertz et al., 2010a ; Vidaurre and Blankertz,
2010 ). Another disadvantageous factor is that it require s prolon ge d training time for the user
to learn to control the BCI s y st em. How ever, usi ng advanced signal processing and m achine
learning al gorithms, the amount of training is inc reasingly reduced to zer o (B lankertz et al.,
2006a ).
These ERD/ERS modulations can be investigated b y anal y zin g the Ev ent -Related
Spectral Perturbations, ERS P (Section 3.3.4.1 ) or the envelope of th e signal filtered in the mu
band (Appendix A.1.3.2 ).

2.5 Notation
21
2.5 Notation
In this sec tion, the concepts and notations used in this thesis are further detailed. Firstly, as
stated before in S ection 2.3 , a ‘ BCI s ystem ’ te rm will refer to the off-line part of a BC I
system, if not specified otherwise. Anoth er noti on used here refers to a mental state, meanin g
at the same time a brain activity pa ttern.
The detailed notation used in this manuscript for equations is presented i n the table
below (T ab. 2.1 ). Matrices are denoted with boldface uppercase letters, v ectors as boldface
lowerca s e letters, scalar values as R oman uppercase letters or small Greek letters and indi ces
of vector or m atrices as lowercase italic letters.
Tab. 2.1 Notation

Symbol

Description

X

a matrix (bold capital lett ers), where I denote the i dentity matrix, W
the filtering m atrix, A the patterns matrix

y

a vector (bold small letters), where y (t) denotes a tempora l signal ,
where specifically , b is the bias and z is the z-score

A or α

scalar values (c apital Roman or small Greek letters), where α, β, 𝛶 ,
θ, 𝛿 denote the EEG frequency bands

i, j, k

indices of vectors o r mat rices (small italic Roman letters), except p
which is the probability value ( p -value)

E or N e

number of epochs (trials) of a recorde d si gnal

N or N c

number of EEG rec o rded channels

T or T e

number of samples of a measurement signa l

||.||, |.|

absolute value of a sca la r, vector or a complex norm

||.|| 2

Euclidean distance/L 2 - no rm of a vector or a complex number

‖ . ‖ 2
2

Squared Euclidian distance

||.|| F

Frobenius-norm

<·>

inner product of two vectors

(.) T

matrix or vector transpose

(.) -1

Inverse o f a matrix

(.) +

(Moore Penrose) Pseudoinverse of a matrix

(.) *

complex conjugate of a matrix or vector

∑

Covariance matrix , where e.g. ∑ 𝑘 =1
𝑁 is used to represent the sum of
elements from k =1,.., N

∑


estimated covariance mat rix

𝑃
 ( 𝑓 )

estimated power of th e signa l

F

Fourier transfor m

ℋ

Entropy

𝔼

Expected value

ψ(t)

Mother wave l et

φ( t )

Father wavelet

MI

Mutual I n formation

Chapter II. Fundamentals of neurophy siolo gy
22

f

freque n c y

μ

mean

σ

standard deviation

σ 2

variance

x ∼
𝒩
( μ , σ 2 )

multivariate random vari able x, Gaussian (normal) distributed with
mean μ and variance σ 2

λ

eigenvalue, w h ere λ 1 express the largest eigenvalue

sgn

sign func tion

sgn r 2

signed a n d squared point biserial correlation coefficient ( signed r 2 )

log, ln

Natural logarithm

log 10

decimal logarithm

tanh

hyperbolic tange nt

Hz

Hertz – frequency unit in the I nt erna tional S y stem of Units (SI)

dB

Decibels – power measu rement unit in the I nternational S y stem of
Units (SI)

cm

Centimeter – distance unit in the Inter n ational Sy s tem of Units (SI)

ms

Milliseconds – time unit in the I nte rnational S y st em of Units (S I )

23

Chapter 3

Signa l processing and machi ne
learning metho ds in BCI research

For more than a centu ry, scientists investigated brain activit y to gain insights into perce ptual,
cog nitive and motor functions.
This chapter provides a s hort description o f the existing methods and techniques used
to accomplish each step of a BCI s y stem, followed b y an overview of the main BCI d esigns
and applications. The methods describe d here refer to off-line BCI s, but mi g ht be applicable
also for on-line s y stems , with corresponding adaptations. In mor e detail, the chapter will
cover the steps composing a BC I , starting with the measurement types of brain activit y in
Section 3.1 , followed b y the p re-proce ssin g approaches in S ection 3.2 and the feature
extraction methods in S e ction 3.3 , and continuing with the classification techniques described
in S ection 3.4 , comprisin g linear methods ov erall. The last S ection 3.5 pres ents the most used
BCI applications developed, by emphasizing the possi ble applications related to the
experimental designs proposed in this thesis.
3.1 Brain activity measure ment
Various techniques have been dev eloped that m easure the brain activit y (de Moor, 2003 ;
Wolpaw et al., 2006 ). Some of them require invasive methods, such as:
ElectroCorticoGraphy (EC oG) (Leuthardt et al., 2006 ) where a grid of electrodes is placed
over the dura-mater, or i mplanted electrodes placed under the skull ( L e bedev and Nicol elis,
2006 ). As non-invasive techniques, w e remind the hemod y namic me asurements, such as
MagnetoEnce ph aloGraphy (MEG ) (Mellinger et al., 2007 ; Besserve et al., 2008 ), functional
Magnetic Resonance Imag in g (fMRI) (Weiskopf e t al., 2004 ) or Ne ar InfraRed Spectroscop y
(NIRS) (Coyle et al., 2007 ). Also non -invasive is ElectroEncephaloGraphy (EEG) (W olpaw
et al., 2006 ), one of the most widely used acquisition technique due to its re lativel y a cce ssible

Chapter III . Si gnal processing and machine learning methods in BCI resea rch
24
price, non-invasiveness, portabilit y and a v ery good tempor al resolution. I n this thesis
research, we have restricted to EEG as an acquisition measure for the BCI designs, due to its
numerous advantages.
3.2 P re processing
3.2.1 P rel im inary filtering and p reprocessin g
After the brain si gnals have been recorded using the measurement t y pes (i n this case, EEG),
the raw signals must be cleaned and denoised of unwanted perturbati ons. A preliminar y
filtering of the raw signals is performe d online b y th e hardware while the signals are
recorded. Usually, it consists of a combination of a high-pass filter ( HP ) o f 0.1 Hz or less, a
low pass fi lter (LP) and a notch filter (LPN), in order to remove the int erfering frequencies,
the D C ripple a nd c ables movement artifacts.
Ne xt, offline filtering o f th e input signals is needed to have a clearer si gnal and to
increase the S igna l to Noise R atio (SNR). This is done by temporal ( Section 3.2.1.1 ) or
spatial filters (Section 3.2.2.2 ) or even the combination of both if the signa l is nois y or
contains movement artif acts, for example. Regarding temporal filters, the idea is to perform
band pass filtering or a s uccession of high-pass and low -pass filtering in the frequenc y band
of interest, which are fu rther described below.
3.2.1.1 Temporal filters
The temporal filters can remove various undesired effec ts such as slow variations in the EEG
signal, caused by elec tro des polarization or b y power -line inte rference (50 Hz in Europe). I n
addition, they c an reduce the influence of nois y frequencies that are out side the frequenc y
range o f the brain activ ity investigated. Gener ally, th e filtering can be achieved using
Discrete Fourier Transform (DFT) (Appendix A.1.3.1.1 ), F inite I mpul se Re sponse (FI R)
(Appendix A.1.3.1.3 ), Infinite I mpulse Response (IIR) filters (Appendix A.1.3.1.4 ) and man y
others. Because the filters ma y introduce artifacts and phase shifts, strong ly altering the
neural signals, it is advisable to apply the filters in reverse i n the offline s cena rio in order to
produce a zero -phase sh ift. This tactic is not applicable in the online case, therefore causal
filters must be considered (which do not depe nd o n the future inputs) (Le m m et al., 2004 ).
3.2.1.2 Downsampling
In general, the EEG signals are r ecorded with a sampling rate o f 1000 Hz for a hi gher signal
quality (given 500Hz maximum ba ndwidth ac cording to the Ny quist frequency) and are
amplified with an order of 20000 fr om tiny nanovolts to microvolts, so they can be easil y
investigated in the signal analysis on a bigger level. Further, be cause the human brain
ge n erates frequencies b etween 0 and 100Hz ( Niederme y er and Sil va, 2005 ), the signals are
usually downsampled to 100 Hz or more after filt ering, in order to reduce the amount of data
and to keep only the necessary information.
3.2.1.3 Re -refe rencing
In order to reduce the perturbation s in amplitude produc ed b y th e hardware (different
voltage s and elec trod e c onductivities between single cha nn els recordings ), the si gna ls can be

3.2 Preproce ssin g
25
offline re -referenced to a baseline (Delorme et al., 2011 ). W hile for the EE G data acquisition,
the volt ag e is alread y me asured with respec t to a reference electrod e, ele ctrode measurement
which can be comp romised due to artifacts o r el ectrica l activit y , affecting the refore all the
other elec trod es, re-referencing aims at mitiga ting this effect (Lepage et al., 2014 ). For
example, referencing to electrodes placed on t he mastoids (linked ma stoids), to forehead
reference electrodes, or even to a n average channels sign al ( common av erage referencing)
can be pe rformed. This can be achieved b y line arly transformin g the recorded data, namel y
by subtracting the reference signa l f rom each EEG channel (Tallon-Baudry et al., 2001 ; Luc k,
2014 ; Staudigl et al., 2015 ).
3.2.1.4 Baseline correction
After the se gmentation of the d ata (where each trial includes a p re-stimulus and a post -
stimulus interva l), baseline correction (Kronegg et al., 2007 ) regarding the selected pre-
stimulus interval is performed for each trial. T he reference interva l, also referred to as
baseline interval, is usually selected from -200ms or -100ms to zero ( where zero is the
stimulus onset ). An averag ed amplitude or sp ectrum value computed on this refe r ence
interval is subtrac t ed fr o m each tri al, aiming at diminishing the non-stationarity effects of
EEG signals and re du cing the backg round noise activity.
3.2.2 Enhanc ed artifact rem oval
As mentioned in the aim of the thesis, one important aspect that need be considered when
developing a BCI s ystem is related to it s decision basis, such that it is not base d on sig nals
that do not constitute cortical ori gins. I n addition, the aim is to create a s ystem that does not
act as a ‘black box’ s y st em, but rather an interpretable BCI wh ere researchers can visualiz e
and interpret what th e BCI has detecte d. Purs uing this goal, the EEG artifact correction
(Section 3.2.2.1 ) and source loc alization functions (Section 3.2.2.2 ) are mandator y in a BC I
system.
Different t y pes o f artifa cts (F isch and Spehlmann, 1999 ) affect the tas k-relate d or
mental state brain activit y , produced b y electronic devices, e.g. loos e electrodes, out er
electric fields, drifts ; or b y biopot entials generated b y pa rticipant ’s bod y such as: e y e
movements, muscular activit y , etc. C orresponding filters are applied depending on the t y pe of
noise: FIR/IIR filters for removing electronic noise, and adaptive filters for rejecting the bod y
biopotential artifacts. These artifacts a re characterized by hi gh amplitudes and frequenc y
( ≫ 100µV and > 60 Hz ) exceeding the neural a ctivity ( ≤ 50µV in adults ; < 80 Hz )
(Muthukumaraswa m y, 2013 ).
The data is primaril y filtered in the nec essary frequenc y range (below 50Hz)
corre spondin g to the investigated n eural activit y (Section 3.2.1.1 ). Therefore, a part of the
freque n cies related to body biopotential artifacts (e.g . muscular activit y) are automaticall y
excluded.
Further , a rough pre-cle aning of the data is necessary to be performe d in order to
improve the qu ality of t he data b y rejecting th e nois y epochs (trials) and channels while
keeping onl y th e good q uality ones . Secondl y , p rojection methods are imp lemented to ex tract
the releva nt br ain ac tivi t y and discard the noi sy a ctivity ( ar tifactual sources). A further
description of these last two approaches follows below.

Chapter III . Si gnal processing and machine learning methods in BCI research
26
3.2.2.1 Rejection Methods
The rejection methods ( Muthukumaraswam y , 2013 ; Samek, et al., 2017 ) detect the artifactual
epochs or channels and r emove them based on thresholds given b y spe cific characteristics of
the artifacts, such as high amplitude or high freq uency, depending on th e t y pe of artif acts; or
by anal y zing the deviation of the signals. Two approaches used in this thesis are further
described below, namely max-min and variance criterions.
1. Max-min c rit erion
The max -min method rejects artifactual epochs by analyzing the features of an epoch
and detecting if it’s out of a normal threshold range . For example, for strong e ye movement
artifac ts which are consi dered greater than 100 µ V in amplitude, it is implied a threshold of
maximal difference o f about 150 µV between t he maximum and the minimum amplitude
values fo r one epo ch, searched within the electrodes providin g info rmation o ver v ertica l
(AF3, AF4, Fp1, Fp2 and EOG channels) and horiz ontal eye movements (F9 and F10) . If the
maximal difference of th e epoch in at least one channel exceedsthe threshold , then the epoch
is discarded from analysis.
2. Variance criterion
In addition to the frequency filterin g described i n Section 3.2.1.1 , which removes a
portion of the artifacts with higher frequencies (> 50Hz) , a further check o ver the signals has
to be performed for the artifacts (e.g. jaw clen ching in the 20 -40 Hz range (Khoshnam et al.,
2017 ) which are interleaved with the neural related frequenc y . The solut ion involves a
variance check over the broad band-power 5-40 Hz. The epochs are rejected when are
character iz ed b y excessive variance in more than 20% of the channels. In addition, channels
dropping to zero (loose electrodes) represented by variance lower than 0. 5µV² in more than
10% of the trials were also removed.
3.2.2.2 Projection Methods – spatial filters
In contrast to the rejection approach, projection methods do not remove artifactual epochs,
but the artifactual sour ces ba sed on decomposi tion. This provides a spat ial filtering of the
data, resulting in a c le aner EEG most likely composed by neura l sour ces.
The recordin g of the e le ctrical brain activity is strongl y influe n ced b y th e e y es and
muscles moveme nts (Fatourechi et a l., 2007 ), especially that these artifacts h ave higher
amplitude and cover u p the neural activit y . More over, it is mandatory to neglect the
background brain activity th at is not related to the neural activit y of interest , procedure that is
not covered b y the reject ion methods . Removing the undesired noise, incr eases the signal to
noise ratio of the signals and reduces the effect of volume condu ction. Th is is performed b y
temporal, spatial (Parra et al., 2005 ) or sp ectral filters (McFarland et al., 19 97 ; Ramoser et al.,
2000 ; Le mm et al., 2005 ; Dornhege et al., 2006 ; Tomioka et al., 2006 ), such as Independant
Component Anal y sis ( Makeig et al., 1996 ; Makeig et al., 2000a ; Naeem et al., 2006 ;
Kache nou ra et al., 2008 ) , blind source sep aration (Ziehe and Müller, 1998 ) , Common Spatial
Patterns (Ramoser et al. , 2000 ; Dornhege et al., 2004a ; Blankertz et al., 2008a , c ; Grosse-
Wentrup and Bus, 2008 ), Spatio -Spectra l Decomposition (Nikulin, et al., 2011 ), etc.
Moreover, the spatial filtering (CSP ) contributes to the enhance ment of the BC I performance

3.2 Preproce ssin g
27
(Dornhege et al., 2004b ; Blankertz et al., 2006c ) . Altogether, the application of spatial filters
is highly recommended for EEG analysis.
1. Independe nt component analysis (ICA) – Infomax
A well-used method that separates the sign al into artifa ctu al components a nd neuronal
activity assuming indep endently generated sources is I nd ependent component anal y sis ( I CA )
(Makeig et al., 1996 ; Hy vä rinen et al., 2004 ). By separating the mixed s igna l into additive
subcomponents, it attempts therefore to solve the ‘cocktail part y ’ problem. The basic
assumption that the anal y sis is based on, considers non-Gaussian subcomponents and
statistically independent sources

1

. I CA considers two choices for independence (Haykin,
2009 ): minimizing Mutual Infor m ation ( MI ) (e. g. Infomax algorithm - Bell and Sejnowski,
1995 ; Amari et al., 19 96 ) or max imizing non-Gaussianit y (e.g. Max imum Likelihood
estimation – Stone, 2004 ; FastICA a l gorithm - Hyvärinen and Oja, 2000 ).
In g en era l, the ICA algorithm can be describe d as follows. For a random variable
repre s ented b y the vector x = [ x 1 , ..., x m ] T and the source components that we want to extract
as s = [ s 1 ,..., s n ] T , the generative forward model is ex pressed by x = A · s , w ith A being the
mixing matrix , whe re the independent components are detected by m aximizing the cost
function. Considering ze ro-mean and un correlated Gaussian noise n ~
𝒩
(0, σ 2 ), the related
equation is: x = A s + n , where t he component x is c ompose d of the s um of independent
components 𝐱 = ∑ 𝐚 𝑘 𝐬 𝑘
𝑛
𝑘 =1 . The original sources s can be recovere d by multiply in g the
observed signals x with the inverse of the mixing matrix W = A -1 , also known as the
unmixing matrix. Therefore, thi s is performed b y means of a linear transformation, s = W T x
+ n , termed the backward model.
Considering the I nformax approac h (Be ll and Sejnowski, 1995 ; Amari et al., 1996 ) ,
ICA acts like a multivariate projection al gorithm, extracting M multiple signals in parallel ,
whereas the projection ( W ) extracts a successi on of signals ( y ) from a set of M signal
mixtures. S tarting from the set of signal mi xtures x and a mut ual indep endent set g given b y
Cumulative Distribution Functions (CDFs), the aim is to detect the unmix ing matrix W that
maximizes the joint entropy of the signals Y = g ( y ), where y = W T · x . Based on the optimal
unmixing matrix W , the signals Y are independent c haracterized by maximum entropy ,
impl y in g independenc y a lso in the extracted signals y = g -1 ( Y ). When the source Probabilit y
Density Function (PD F) of the source 𝑝 (𝐬) fits the PDF of the extracted si gna l 𝑝 (𝐲) , then the
maximization of the joint entropy of Y also max imizes the mutual infor mation MI ( x , Y ).
Now, the entropy of the signals Y = g ( y ), can be assessed b y :

1

With r espect to EEG, the signals ge nerated from d istinct sources pr opagate through the co rtex and mix up
towards the surface o f the scalp where they are recor ded. Therefore, the EE G signal s are c onsidered as a linear
mixture o f un known so urces t hat ca n be solved b y bli nd so urce sep aration. E ven t hough the si gnals cor relate in
their f low o f i nformation ( Makeig et al., 2004 ), the main assumption of IC A re garding spatially independen t
sources holds for cortical areas while t hey ar e spatiall y an d neuroanato mically di fferentiable. For this reason,
components such as e ye m o vements can be separ ated from neural co mponents. Furthermore, the ICA unmi xing
process can be performed not only in the spatial do m ai n (spatial ICA), but also in the tem po ral domain
(temporal ICA, Jung et al., 2000 ), where the ass umptions co nsider te m po rally ind ependent underl y i ng
components with possible overlap ping spatial topograp hie s and is g e nerally app lied for discriminating ERP
components.

Chapter III . Si gnal processing and machine learning methods in BCI research
28
ℋ ( 𝐘 ) = 1
𝑁 ∑ ln (𝑝( 𝐘))
𝑁
𝑘 =1 , (3.1)
where 𝐩 𝐘 is given b y : 𝑝 (𝐘) = 𝑝( 𝐲) | 𝐉 | ⁄ , with | 𝐉 | = | ∂𝐘 ∂𝐲
⁄ | = 𝐠 ′ ( 𝐲 ) = 𝑝( 𝐬, 𝐲) the
Jacobian matrix. This gives:
ℋ ( 𝐘 ) = − 1
𝑁 ∑ ln ( 𝑝(𝐲)
𝑝(𝐬, 𝐲) )
𝑁
𝑘 =1 (3.2)
When PDF( 𝑝( 𝐬) ) fits PDF ( 𝑝 (𝐲) ), 𝑝( 𝐘) has an uniform distri bution and ℋ ( 𝐘 ) is
maximized. Then, based on: 𝑝 ( 𝐲 ) = 𝑝 ( 𝐱 ) | ∂𝐲 ∂ 𝐱
⁄ | ⁄ = 𝑝 ( 𝐱 ) | 𝐖 | ⁄ (3.3)
the entropy of Y is:
ℋ ( 𝐘 ) = − 1
𝑁 ∑ ln ( 𝑝 (𝐲)
𝐖∙𝑝(𝐬,𝐲) ) =
𝑁
𝑘 =1 1
𝑁 ∑ ln ( 𝑝 (𝐬, 𝐲) ) + ln (| 𝐖 |) + ℋ ( 𝐱 )
𝑁
𝑘 =1 . (3.4)
In the end, ℋ ( 𝐘 ) is maximize d to accomplish the independe nc y of y . ℋ ( 𝐱 ) can be
ignore d in this c ase, because it is not affected.
Now for M signa l mixtures, 𝑝 (𝐬) can be ex pressed by a logistic function, usuall y
chosen as the h y perbolic tang ent function, tanh : 𝑝 ( 𝐬 ) = (1 − 𝑡𝑎𝑛ℎ (𝐬) 2 ) . The entropy of Y
is: ℋ ( 𝐘 ) = − 1
𝑁 ∑ ∑ ln (1 − 𝑡𝑎𝑛ℎ ( 𝐰 𝐢 𝐓 𝐱 (𝑘 )) 2 ) + ln (| 𝐖 |)
𝑁
𝑘=1
𝑀
𝑖 = 1 . (3.5)
And the optimal unmixing W c an be found using the gradient descent method:
W m +1 = W m + λ m ( I − tanh ( Y ) Y T ) W m . (3.6)
After the ICA de composition, a decision has to be made regarding the t ype of
component: neural o r artifactual. This decision a nd selection of the neural components to be
kept is performed in two manners: manuall y or automatically. The m anual selection inspects
the components by checking the spatial pa ttern and the power spectrum. Whil e this approach
requires lon g er time as well as scientist expertise, an automatic app roach suits better in this
context. One good approach in thi s sense is im plemented by Winkl er et al., ( 2011 ), algorithm
named as Multiple Artifact Rejec tion Algorithm (MARA).
2. ICA with automatic artifactual component selection (MARA)
The Multiple Artifact Rejection Algorithm ( MARA) (Winkler et al., 2011 ) detects the
artifac tu al ICs ( Independent Components) using a classifier based on six features. One
feature represents the ICs temporal evolution and targets outliers’ detec t ion based on mean
local skewness. Three f eatures relate to the power spectrum, in which two characterize the
distribution of the normal logarithmic decreased s pectrum shape; and one detects the standard
alpha peak specific to neural components, based on the avera ge logarithmic power of the
alpha band (8 -13 Hz). T wo features identif y th e spatial dist ribution of the ICs, in which one
indicates the source distribution and its type based on l 2 -norm (neural source given b y
minimal l 2 -norm and artifa ctual signal by maximal l 2 -norm); and one d eter mines localized
spatial distributions which refer to loose ele ctrodes or muscle artifacts, providing additional
information of source location s as compared to the I CA method which is computed by means
of the logarithmic difference be tw een the maximum and minimum activation in a sca lp map.
Overall, its application successfull y cleans the EEG data of small e y e movement
artifac ts, m uscular artifacts and loose electrodes.

3.2 Preproce ssin g
29
3. Spatio-Spectral Decomposition (SSD)
In most cases, the neur al activity of interest overlaps with the background noise
activity, ther efore enhanced separation tec hniqu es are requested. Based on the premise that
noise sourc es outsprea d ove r few Hz or even tens of Hz and knowin g that are usuall y
modeled as white or 1/f noise, the noise in terferences can be reduced or even canceled b y
inhibiting the noise in the spectral neighborhood of the frequency ra n ge of int ere st that
character iz e the inv estigated neural process. Mathematicall y , th e Spatio-Spectral
Decomposition (SSD) method (N ikulin et al., 2011 ) re presents a li near decomposition
algorithm that maximizes the signal variance of a desired frequency band , while
simultaneously diminish es it at the neig hborin g noise freque n cies, e nh ancing therefore the
signal- to -noise ratio. S SD enhances the extracti on of oscillator y activity, especially in the
alpha band ch aracter iz ed by the alpha p eak and i t has be en shown that SS D performs better
than the I CA method (N ikulin et al., 2011 ; Wink ler et al., 2015 ). Moreover, SSD could be
further us ed as a dimensionality r eduction method . For mor e details on this aspect, ple ase se e
the heuristic dimensionality reduction approach proposed b y Haufe et al., ( 2014a ).
Given a set of recorded signals X of siz e t × c , with t – the number of samples and c –
the number of channels, the measured signal X is filtered in the f requency of interest f, giving
X s and in the neighboring frequencies f ± Δ f (with Δ f in the ran ge 1 -2 Hz) which will be
considered further as noise, resulting in X N . Filtering in the frequency of int ere st is performed
by band-pass filterin g of f (e.g. 8-13 Hz) and the neighboring f requencies (the left and ri ght
side bands) can be obtained b y appl y in g a band -pass filter on the entire range [ f −Δ f; f +Δ f ]
and subsequentl y appl y ing a band-stop filter for removing the band of interest and k eeping
only the si gnals in the n eig hborin g frequencies (e.g. 7 -8 Hz and 13 - 14 Hz for Δ f = 1 Hz).
Now, denotin g the cov ariance matrices of th e filtered si gnal of interest and the filtered signal
noise, b y ∑ S and ∑ N , the aim is to find the spatial filter w that maximize the signal to noise
ratio (SNR) between the variance of the frequency of inter est and the variance of the noise
(the surroundin g frequenc y bins). The max imization of the SNR of the projected signal can
be defined by max imizing the objective func tion :
𝑆𝑁𝑅 ( 𝐰 ) = max
𝐰 𝜎 2 ( 𝐰 𝑇 𝐱 𝐒 )
𝜎 2 ( 𝐰 𝑇 𝐱 𝐍 ) = max
𝐰 𝐰 𝐓 ∑ 𝐒 𝐰
𝐰 𝐓 ∑ 𝐍 𝐰 (3.7)
Taking the deriva tive w a nd imposing the equa lit y to zero, gives:
λ ∑ 𝐍 𝐰 = ∑ 𝐒 𝐰 , (3.8)
which can be solved b y the g eneralized eigenvalue decomposition (GEVD) (Francis, 1961 ;
Kublanovskaya, 1962 ), where λ is the generalized eigenvalue related to the eigenvec tor w .
For neuroph y siolo gical investigation, th e spatial patterns A , whe re each column of A
indicates component’s contributions (strength and polarity) in the measured channels, are
determined by t ransforming the backward models (the filters which can not be interpreted)
into forward models (the patterns which are neuroph y siolo gica ll y interpreta ble) (Haufe et al. ,
2014b ), considering the following transformation: A = ∑ W ∑ c -1 = ∑ W ( W T ∑ W ) - 1 (Haufe et
al., 2014b ), with ∑ b eing the data covariance matrix , W the spatial filter matrix and ∑ c the
covariance matr ix of the component.

Chapter III . Si gnal processing and machine learning methods in BCI research
30
4. Common Spatial Patterns (CSP)
Another powerful technique for spatial filtering is the Comm on Spatial Patterns (CSP)
algorithm ( Fukunaga, 19 90 ; Koles, 1991 ; Ramoser et al., 2000 ; L emm et al., 2005 ; L otte et
al., 2007a ; Blankertz et al., 2008a , c ; Tomioka and Müll er, 2010 ; Sannelli et al., 2011 ; Samek
et al., 2012 ; Vidaurre et al., 2015 ). Th e method is further us ed as a fe ature extraction method
in which the common spatial filters (CSF) ar e applied to extract the neur al sources specific
for class discrimination . Shortl y , CSP facilitates the discrimination of different brain states b y
spatial filtering, enhanc in g the signa l of inte rest while suppressing the background activity.
a. Binary case
For spatial filte ring and as a f eature extraction process, C SP filters are frequently applied in
BCI in order to reduce the effect of volum e conduction and extract the correspondin g
oscillatory features. Moreover, CSP ex tracts class discrimination spatial p atterns that relate to
neural sources. Standardly, the CSP approach functions for binar y c ases, detecting the
discriminative modulations betwee n the two classes. Previously describe d b y Fukunaga
( 1990 ), Koles ( 1991 ), Müller-Gerking et al. ( 1999 ), R amoser et al. ( 2000 ), CSP detects the
spatial projection of th e band-pass filtered data that maximizes the variance for on e class
while minimizing the variance fo r the other class.
Considering ∑ 1 and ∑ 2 as the covariance matrices of the two classes fo r the band-
passed filtered data, on e procedure consists in stimultaneousl y dia gonalizing ∑ 1 and ∑ 2 such
that the eigenvalues sum to 1: 𝐖 𝑇 ∑ 𝟏 𝐖 = 𝐃 1 ,
𝐖 𝑇 ∑ 𝟐 𝐖 = 𝐃 2 , s.t. D 1 + D 2 = I (3.9)
The generalized eigevectors W are computed by :
∑ 𝟐 𝐖 = (∑ 𝟏 + ∑ 𝟐 ) 𝐖𝐃
where D is the diag onal matrix containing the generalized eigenvalues of ∑ 2 (with values
between 0 and 1) and w j , the c olumn vectors of W represent the spatial filters.
The enhanced discriminative activit y between the two classes can b e ob tained as a
ratio between the varian ce of one class and the variance of the joint activit y ∑ 1 + ∑ 2 . Then ,
the objective function for dete cting the w filters that max imize the variance for th e two
conditions is described by :
max
𝐰∈ℝ c 𝐰 T ∑ 𝟐 𝐰
𝐰 T (∑ 𝟏 +∑ 𝟐 )𝐰 (3.10)
This can be resolved by computing the ge neralized eigenvalue problem:
∑ 𝟐 𝐰 = 𝜆 (∑ 𝟏 + ∑ 𝟐 ) 𝐰 (3.11)
which y i elds a set of eigenvectors w i and 𝜆 i eigenvalues for i = 1,.., N wit h N – the number of
channels. The eigenvectors corresponding to the first largest e i genvalues max imizes the
variance for one condition and minim izes the vari ance for the other condition and vice- versa
for the l ast lowest eigenvalues. Hence, the spatial filters that best m aximize the variance
between classes correspond toopposit e eigenvalues. A good practice is to choose several
eigenvec tors from both si des (e.g. up to 6, with three from each side - Blankertz et al., 2008c )
selected bas ed on a s core related to the ratio of th e medians which is more robust to outl iers,
as compared to the classical eige nv alue score.

3.2 Pr eproc essin g
31
After the decomposition and the corresponding common spatial filters have been
extracted and the signals were projected to the CSPs , the feature s are composed b y
considering the band po wer of the detected sour ces, which is generall y approxim ated in BCI
research based on the logarithm of the spatial filtere d da ta (log-power of the band-pass
filtered signal, log( P (x)) ) (Blankertz et al., 2008c ). An important step before CSP filtering
(before estimating th e band-power of the projected signa ls) is represented b y computing a
linear projection of the data, as de tailed in (Dähne et al., 2014b ; Haufe et al ., 2014b ).
Another important remark must be mentioned regarding the evaluation of the CSP
algorithms. Due to the fac t that the CSP technique considers label information, the
computation of the filters is mandator y to be performed on the trainin g dat a, with appropriate
application on the test data b y linear derivation. Otherw ise, it ma y lead to considera ble
underestimation of the genera liz ation err or (Blankertz et al., 2008c ).
Further, it is imperative to notice that the neurophy siol ogical interpretation must focus
only on the spatial pa tterns, without considering the spatial filters which cannot be
interpretable du e to their mosaic spatial structur e, simultaneousl y relating to signal and noise
components (Bießmann et al., 2012 ; Haufe et al., 2014b ). The spatial pat terns can be easil y
computed b y inverse transformation in relation t o the spatial filters: A = ( W T ) -1 . When the
spatial filter mat rix W is not invertible, the ps eudoinverse is computed: A = ( W T ) + . However,
when W does not have full rank, the patterns do not coincide any more with the entries of the
pseudoinverse (Haufe et al., 2014b ), therefore, respective transform ation has to be perf ormed ,
as in Section 3.2.2.2.3 .
Despite the major advantages of the classic C SP algorithm, such as: producing hi gh
signal- to -noise ratio, its computational efficiency and eas y implementation, a n important
challenge arise referring to artifacts and non -stationarit y . Various modifications and
extensions to the CSP algorithm ha ve been proposed to tackle this problem (Lemm et al.,
2005 , 2011 , Dornhege et al., 2006 , L otte et al., 2007a ; Sannelli et a l., 2011 ; Samek et al.,
2014 ). Some of them foc us on invariance and robustness to noise and art ifacts (Blankertz et
al., 2008a ; Kawanabe et al., 2014 ) and others to non -stationarities in the data (Samek et al.,
2012 , 2014 ).
b. Multi-class approach
First, we need to unde rstand wh y a multi-class approach is n ecessary to be performed. While
targeting mul tiple decisions choices to be inferred in the B C I adaptation, the answer comes
from the primar y goal o f a BC I application whi ch requires onli ne imple mentation and real -
time classific ation of the brain signals. Towards t his g oal, a mul ti-class approach suits better
compared to multiple binary discriminations.
While generally suited for binary cases, some CSP extensions have bee n developed
for the multi-class approach (Müller -Gerking et al., 1999 ; Dornhege et al., 2004a ; Dornhege
et al., 2004b ), namel y th e IN approach ( Müller- Gerking et al., 1999 , All wein et al., 2000 ),
One Versus the Rest ap proac h (OVR) (Wu et al., 2005 ) and Simultaneous Diagona liz ation
(SIM) (Grosse-Wentrup and Bus, 2008 ). W hile the I N approach considers reducing the mul ti -
class problem to several binary decisions (Müller-Gerking et al., 1999 , Allwein et al., 2000 )
and requires lon g er ti me to be performed. an app ropriate multi -class approach is necessary to
distinguish faster the cor responding class membership. An appropriate ex tension of the CSP

Chapter III . Si gnal processing and machine learning methods in BCI research
32
to the multi-class problem has been pr eviously considered by (Dornhege et al., 2004a ) and
involves computing the CSPs for each class in r elation to the oth er classes. This method is
referre d to as one over rest (OVR) strateg y . Fu rthermore , a Simultaneous Diagonalization
(SIM) method or Joint Approxim ate Diagonalization (JAD) method ( Cardoso and
Souloumiac, 1996 ; Ziehe and Müller, 1998 ; Z ie he et al., 2000 , Pham, 2001 ; Ziehe et al.,
2004 ; Grosse-Wentrup and Bus, 2008 ), conside rs estimating the C SPs for each of th e multi -
classes. In the presented thesis, the last enha nc ed approac h is furth er investigated.
O ne Versus the Rest CSP approach (OVR)
An improvement to the IN approac h is represented by th e OVR approach ( Dornhege
et al., 2004a ). While I N does binar y classificatio n on all binar y pairs and assigns the tri als to
the class membership ba sed on the highe st voting out of the three classifiers, OVR p erforms
multi-classification on all one versus rest binary CSP patterns. The EEG data is of course
projected onto the CSP s and all the variances, lo g-band power of the C SP features, are fed to
the classifier.
Joint Approximate Diagonalization (JAD )
While in a binary case (IN or OVR) , the CSP filters are computed based on a
simultaneous diagonalization of the two covariance matrices with their eigenvalues sum to
one (Eq. 3.9 ), the multi- class J AD approach (Grosse -Wentrup and Bus, 2 008 ) finds a matrix
that follows the same rule for decomposition but related to multi covarianc e matrices. When
in the binar y case the solut ion can be easily found, in the multi -class approach an
approximation of the solut ion is computed based e.g. on approximate simult aneous
diagonalization. Meaning that for the covariance matrix ∑ of each k class ( k = 1,.., N , where N
is the number of classe s), the decomposition finds an approximation solut ion for the W
matrix that satisfie s 𝐖 T ∑ 𝑘 𝐖 = 𝐃 𝑘 , where 𝐷 𝑘 is a diagonal matr ix fulfilling ∑ 𝐃 𝑘 = 𝐈
𝑁
𝑘 =1 ,
with 𝐈 being the identit y matrix . In our im plementation, this joint diag onalization problem is
computed with the FFDiag (Fast Frobenius Di agonalization) alg orithm ( Ziehe et al., 2004 ) .
The algorithm is based on the Frobenius-no rm formulation and computes the dia g onalization
using non-orthogonal tra nsformation and a recursivit y computation based on a multiplicative
iteration, assuring the invertibility o f W .
In more detail, it finds an approximate solution of the following optimi zation
problem, by minim izing the Frobenius norm of the off-diagonal elements of D k :
min
𝑊 ∈ℝ 𝑀×𝑀 ∑ ∑ ((𝐖 T ∑ 𝑘 𝐖) 𝑖𝑗 ) 2
𝑖 ≠𝑗
𝑁
𝑘 =1 (3.12)
While the above cost function can converge to z ero, the invertibility of th e matrix W
prevents this effect from happening : 𝐖 ( 𝑖𝑡 +1) = (𝐼 + 𝐕 ( 𝑖𝑡 ) )𝐖 ( 𝑖𝑡 ) , where it is the current
iteration and 𝐕 ( 𝑖𝑡 ) is the iteration matrix.
Further, after the approximate diagonalization has found a solution, the relevant
activity sources have to be considered. B eca us e there is no canonical way to choose the
relevant CSP patterns, the selection is performed b y conside ring the first m eigenva lues with
highest mutual informati on (out of max imum M sources). This selection is similar to I CA,
where th ese m sour ces d enote the br ain activity r elated to the co rresponding inform ation on
the decisions and intentions of the BCI use r, and the other brain s our ces t hat do not relate to
the BCI task, are considered as noise sour ces. T his spatial filtering composed b y I CA and

3.3 Fea tu re extraction
33
derived approximation of mutual information to identify th e si gnal subs pace is t ermed as
Information Theoretic Feature Extrac tion ( ITFE) :
𝑀𝐼 (𝑐 , 𝐰 𝑗 T 𝐱 ) ≈ − ∑ 𝑝 ( 𝑐 𝑘 ) 𝑙𝑜𝑔 √ 𝐰 𝑗 T ∑ 𝑘 𝐰 𝑗 − 3
16 ( ∑ 𝑝 ( 𝑐 𝑘 ) ((𝐰 𝑗 T 𝐃 𝑘 𝐰 𝑗 ) 2 − 1)
𝑁
𝑖 =1 ) 2
𝑁
𝑘 =1 ,
where MI ( c , 𝐰 𝑗 T 𝐱 ) is the mutual information of the class label c and the line ar transformation
w j T x , calculated based on neg entrop y fo r each eigenvector w j (column j = 1,.. M ) of W , with
𝐰 𝑗 T ∑ 𝑘 𝐰 𝑗 = 1 ; and p(c k ) is the class probability. For more details, see Grosse-Wentrup and
Bus, ( 2008 ).
In the end, a set of optimal linear spatial filters can be interpreted as the m columns of
W with the highest mutual information.: MI ( c , 𝐖 T 𝐱 ) = ∑ 𝑀𝐼 (𝑐 , 𝐰 𝑗 T 𝐱)
𝑚
𝑘 =1 . Therefore ,
according to th e ICA model, all the important inf ormation on the classes is contained in the
first m sources (the first m ICs).
3.3 Feature extraction
The purpose of the feature ex traction step for data anal y sis is to detect the specific values and
character istics of the n eural signals in the temporal, spatial and spectral doma in that best
character iz e the investiga ted neural activit y, whi l st discarding the artifac ts and background
noise of the EEG. These values, termed ‘features’ are then stored in a ‘feature vec to r’ which
is further used for classification.
While some researchers focus more on the classification step of a BCI, granting more
attention to the preprocessi ng and feature ex traction steps is more important in a BCI s ystem
in order to identify and select the optimal neural feature s , which will automatically lead to a
correct and enh anced classification performance (Pfurtscheller et al., 2003 ; Hammon and de
Sa, 2007 ). These features should relate to the neuroph y siological si g nal s that describe the
corre spondin g mental st ate or brain activit y , and not to other activities or bod y p otential
artifac ts.
In the followin g , a description of the extraction methods used through this thesis is
further pre s ented, c onsi dering the temporal (Section 3.3.2 ), spatial and spe ctral (Section
3.3.3 ) signals characteristics.
A good improvement for the brain analy sis and for the classification performance of a
BCI is represented b y combined feature approaches such as spatio -temporal, spatio-spectral,
tempo-spectra l methods and so on (Dornh ege et al., 2004a ; Gysels and Celka, 2004 ; Boostani
et al., 2007 ).
In this case of mul ti-modal features, especiall y in case of features with different t y pe
of units, normalization should be performed on the combined feature v ector. The feature
types have to be centered around zero with a standard deviation of one, in order to have the
features on the same sc a le which will ease the classifier decision. A common approach is to
use z-score normalization whic h is computed b y subtracting the mean a nd dividing b y the
standard deviation for each fe atu re type: z = ( x - µ)/σ .

Chapter III . Si gnal processing and machine learning methods in BCI research
34
3.3.1 F ea ture selection an d dimensional ity reduction
An often-encountered problem in the BCI r efers to hi gh feature vectors dimensions,
especially for multi-modal analysis which combin e different t y pes of features such as spatial,
temporal, or spectral, leading in the end to a n increase in the computation time of the s y stem
or an overestimation of the data considering class ification. In order to treat thi s problem, the
feature vector should b e reduced to an adequate number that in general will improve
performa n ces. Therefore, feature s election and d imensionality reduction b ecome a preferable
approac h (Millán et al., 2002 ; Garret et al., 2003 ; S chrode r et al., 2003 ; Subasi 2010 ; Nikulin
et al., 2011 ; Haufe et al., 2014a ). When reducing the number o f f eatures, i t is also important
to relate the number of fe atures to the amount of data, such that the dimensionalit y of the
training data in one class exceeds the number of f eature s with a couple of factors. Otherwise,
the classification will overfit, issue caused b y the ‘course o f dim ensionality ’ phenom e na
(Frie dm an, 1997 ; Jain et al., 2000 ).
Throug hout thi s thesis, fea ture selection methods , manually o r heuristically
implemented, have been used to reduce the feature vector dimensionalit y , and are described
in the corre spondin g feature extraction or spatial filtering method, e.g. ICA, SSD, CSP.
3.3.2 Tem poral methods
Considering ERP based BCI paradigms, the relevant ERP amplit udes may be considered for
the fea tur e extraction step. The amplitude evolutions of the sig nals within each epoch are
carefully investigated. Preliminaril y , the bas eline correction is applied (Sec tion 3.2.1.4 ). For a
closer inspection and ov erview of the ERP s, a visual repre s entation is usually carried out
referring to average trial s for all trial repetitions. I n this sense, a v eraged sing le-p articipant
ERP repre sentations can be anal y z ed or Grand Averages (GA) considering all participants .
Next, releva nt time intervals are manua ll y or aut omatically dete cted, and the corr esponding
temporal amplit udes are considered as features. W hile the manual procedure requires
additional involvement from the researcher side, automatic methods (e. g . based on
discriminability me asures) are more efficient regarding feature ex traction.
3.3.2.1 Spatio-temporal fe atu r e detection based on signed r 2 discriminability
measurement
Spatio-temporal features are ex tracted considering a heuristic sele ction of the int erva ls with
maximum discriminability and a constant spatial pattern between the two classes, based on
the method presented in Blankertz et al . ( 2011 ). Relevant time intervals are selected with high
signed a n d squared point biserial correlation coefficient ( si gned r 2 ) values.
1. The signed r 2 discriminability measure
For binar y dis crimination between classes, the signed r 2 measure can be a pplied. Considering
two signals x 1 and x 2 , an d their class membership label y 1 and y 2 that relate to two dif ferent
classes (class 1 and class 2), t he signed r 2 measure detects the high differences b etwee n the
signals based on the point biserial correlation coefficient, r ( x 1 , x 2 ). The signed and squared r
value is given by : 𝑠𝑖𝑔𝑛𝑒𝑑 𝑟 2 ( 𝐱 1 , 𝐱 2 ) = 𝑠𝑔𝑛 (𝑟 ( 𝐱 1 , 𝐱 2 ) ) ∙ 𝑟 ( 𝐱 1 , 𝐱 2 ) 2 , (3.13)

3.3 Fea tu re extraction
35
where the point biserial corr el ation coefficient is computed b y considering the signal and th e
class label information:
𝑟 ( 𝐱, 𝐲 ) = √ N 1 ∙N 2
N 1 +N 2 ∙ 𝜇 1 −𝜇 2
𝜎 (𝐱) (3.14)
with N 1 and N 2 – the numbers of samples in class 1 and 2, and µ 1 , µ 2 – the mean of class 1 or
2, respec tivel y.
This discriminabilit y me asure can be also applied in the frequenc y domain , in order to
detect relevant frequency bands with hig h class differentiation of the osc illator y signals
(Blankertz et al., 2006b , 2007 , 2009 ).
2. Discriminability matrix visualization of signed r 2
For visualization purposes, the tempor al and spatial discriminability can be graphically
visualized as time evolution and scalp plots. For a more complete ove rview of the temporal
distribution within each cha nnel , which is not observed in the sca lp plots that presents
information only for a short time interval (e.g. F ig. 5.7 upper plots ) and ne ither in the
temporal evoluti on plot s that shows the information only for one or f ew cha nnels (e.g. Fi g.
5.7 bottom plots), a more detailed representation is given by the discriminability matrix (e.g.
Fig . A.3.7 ), where the s igned r 2 information of ea ch time point is graphicall y represented
with a colormap for all channels, a time versus channels representation.
3.3.3 Spectr al methods
Besides the temporal domain, the spec tr al information also provides valuable information,
which may be complementar y to the temporal features depending on the t y pe of brain
information investiga ted. An overvie w of the extracted spe ctral feature is prese nted in the
following.
3.3.3.1 Band power features
A g ood characterization of the neu ral oscillations can be ex presses by the power of
repre s entative frequenc y bands. Th e si gnal is therefore band -pass filter ed in the relevant
freque n c y band and th e band power features are computed by squ aring the resulted signal or
extracting the lo garithm of the band-power of the signals or compon ents as specified in
(Blankertz et al ., 2008c ), in order to obtain an approximate normal distribution of the features
(Pfurtscheller and Neuper, 2001 ). Different frequenc y b ands may be considered for feature
extraction, depending on the analy zed BC I t ask or mental state, for example the µ fre quenc y
band for motor imagery t asks (Pfurtscheller and N euper, 2001 ; Scherer et a l., 2008 ; Zhong et
al., 2008 ; Nicolae et al., 2016b ) and a various ran g e of frequenc y b ands such as theta, alpha,
beta fo r cognitive processing tasks (Palaniappan, 2005 ; Lotte et al., 2007a , b ; Nicolae et al.,
2016a , 2017a ).
3.3.3.2 Power spectral density features
In order to obt ain more i nformation over brain os cillations, the power spe ctrum also referred
to as the Power Spe ctral Density (PSD) is often ana l y zed in the BCI re searc h (Keirn and
Aunon, 1990 ; Millán et al., 2002 ; Millán and Mouriño, 2003 ). I t shows th e dist ribution of the
signal power over different frequencies and can be computed with the Fourie r transform
(Appendix A.1.3.1.2 ), periodogram (Appendix A.1.3.3.1 ), or an y othe r time to freque nc y

Chapter III . Si gnal processing and machine learning methods in BCI research
36
transformation. The PSD fe atures are then obtained, for example, by taking the filtered
signals or the square o f the filtered signals (Lalor et al., 2005 ).
3.3.4 Tim e-frequency me asures
The neu rophysiolog ic al signals pres ent differ ent characteristics in t he t ime and also
freque n cy domains, the refore another feature extraction method re lat es to a combined
temporal and spectral approach, named time- frequency decomposition. The approach allows
the simultaneous an alysis in both ti me and frequenc y domains v ia ti me – frequency
repre s entations (TFR) (Cohen, 1995 ; Sejdić et al., 2009 ), like Short-time Fourier T ransform
(STFT) (Appendix A. 1.3.4.2 ), W avelet Transform (WT) (Appendix A.1.3.4.3 ), or
repre s entations based on Power Spectral Density (PSD) (Appe ndix A.1.3.4.1 ).
One measure that helps desc ribin g and visualiz ing the chan g es in the po wer spectrum
related to an event is the Event-Related Spe ctra l P erturbation (ERSP) method (Makeig, 1993 )
computed based on a sp ectrogram (more details in Section 3.3.4.1 ).
Another f requency me asure that is commonly used in BCI for measuring the
interactions between signals, for example the ph ase s y nchroniz ation or coherence b etween
channels or epochs at dif fere nt time points is given by the Inter -Trial Cohe renc e (G y sels and
Celka, 2004 ) measure, further de sc ribed in Section 3.3.4.2 ).
3.3.4.1 Event-Related Spectral Perturbations (ERSP)
The Event Related Spectral Pertur b ation ( ERSP) method, introduced by Makei g ( 1993 ) ,
quantifies amplitude dynamic changes of the EEG frequency spectrum in ti me, triggere d b y
an external or internal event. As well known in t he scientific literature, th e oscillations var y
with multiple frequency bands and the ERSP method allows the simultane ous investigation of
the full spectrum, as compared to the narrow-b and ERD/ER S curves, fo r ex ample. I t shows
valuable applications in prac tice (Makeig, 1993 ; Makeig et al., 2004 ; Fuentemilla et al., 2006 ;
Huang et al., 2007a , b ; Li et al., 2011 ; Nicolae, 2013 ; Nicolae et al., 2015c ).
The computation of ERSP starts from generating t he power spectrum of an epoch (the
time which follows an event) o r a continuous s igna l usin g Short-Time Fourier Transform
(STFT) or Wav elet Transform. I n more d etail, a signal (or epoch) is split into overlapping
segmen ts of a g iven window leng th and the average amplitude spectra of these windows is
computed. From each ti me point of the spectrum, the average baseline spectrum computed on
the baseline interva l (the time that prece des the event) is then subtrac ted in order to reduce
the signal background p erturba tion . I n this sense, a simil ar and preferred approach is to
normalize the signal (or epoc h) sp ectrum by division with the a verage base line spectra.
Finally , the lo garithmic spectral amplitude 10*log 10 (power) dB is represented in a ti me by
freque n c y plan e, called spectrogra m . For a general overview of the p erturbat ions for on e
class, the ERSP of the corresponding epochs are then averaged. The time-frequency
repre s entation provides then a larger overview of the Event -Related (D e)Sy nchronization
(ERD/ERS) phenomena (Pfurtscheller and Aranibar, 1979 ) in multiple frequency b ands and
their durations and late n cies, simult aneously .
The mathematica l fo rmulation of the ERSP (Delorme and Make i g, 2004 ) is given by:
𝐸𝑅𝑆𝑃 ( 𝑓 , 𝑡 ) = 1
𝑇 ∑ | 𝑃 𝑘 (𝑓 , 𝑡 ) | 2
𝑇
𝑘 =1 , (3.15)
where P k is the spe ctrum of epoc h k = 1,.., T for the fre quenc y f and time t .

3.4 Classification and Reg ression
37
3.3.4.2 Inter-Trial Coherence (ITC)
The ERD and ERS phenomena are time locked t o a stimulus, not phase locked to an event,
therefore a good measure investigates th e local phase coherence across consecutive t rials,
namely Inter-Trial Cohe rence (ITC) or ‚phase - locking factor’ (Tallon-Baudry et al., 1996 ;
Delorme and Makeig, 2004 ; Makeig et al., 2004 ). I n c ontrast to the ERS P method (Sec tion
3.3.4.1 ), the ITC calculates the EEG phas e coherence between trials for a spe cific
Independe nt Component, channel, time point or frequency interval and ma y indicate the
timing of firing of neu rons groups. I TC is a frequenc y measure of the neural activit y
sync h ronization for a given time point and frequency for di fferent time locked EEG epochs .
Mathematically, ITC is de fined b y the power spectrum, normalized by the Root Mean Square
(RMS) power of single trial estimation:
𝐼𝑇𝐶 ( 𝑓 , 𝑡 ) = 1
𝑇 ∑ 𝑃 𝑘 (𝑓,𝑡 )
| 𝑃 𝑘 (𝑓,𝑡 ) |
𝑇
𝑘 =1 (3.16)
where | |, in this case, is the complex norm. For a specific ti me-point, ITC measure ranges
from zero to one, ex plicitly from no s y nchroniz ation between the EEG epochs to strong
sync h ronization. For a given frequency range, it provides the magnitude and phase of the
spectral estimation. More over, phase coherence between trials can be also e stimated by I nter-
Trial Phase Cohere n ce (ITPC) showing the event-related phase representation.
3.4 Classification a nd Regression
Base d on the optimal feature set detected on the feature extraction and selection processes,
the class discrimination is performed b y means of a classifier in ord er to decode th e
corre spondin g user ment al state or task. For ex ample, in the ERP-based B CI s (Chapter 2.4.1 ) ,
the classifier discriminat es between ta rget and no n -targe t n eural responses, while for mot or-
imagery based BC I s (Ch apter 2.4.2.2 ) it discriminates between differ ent mot or imagery tasks
(e.g. left/right hand mov ement). On a closer look i nto the classifie r process, the data is split
into a labeled trainin g set and a non-labeled test set of feature vectors; and the classifier will
assign the class memberships for the test set considering what it learn ed on the training s et.
Although, various methods have been develop ed for classification (Müller et al.,
2003 ; Lotte et al., 2007a ; Bishop, 2007 ; Lemm et al., 2011 ) or regression (Duda et al., 2001 ;
McFarland, and W olpaw, 2005 ), for example supervised learnin g methods such as L in ear
Discriminant Anal y sis ( LDA ) ( Friedman, 1 989 ; Blankertz et al., 2011 ), Quadratic
Discriminant Anal y sis (QDA) (Mc La chl an, 2004 ), Logistic Regression (LR) (Tomioka et al.,
2007 ), Ridge Regression (RR) (H oerl and Kennard, 1970 ) f ew of them provide hi gh
performing results for EEG data (Bashashati et al., 2007 , Lotte et al., 2007a ). As the
complexit y of a classifi er is increased, so is the generalization error and the classifier
performance will degrade. Therefore, simple li near al gorithms , such as Linear Discriminant
Analy sis (LDA ), are better suited in this context (see also results in Sectio n 5.3.3.3 ). Further ,
referring to the high number of fea tur es that could charac t erize a BCI s ystem, for example a
large set of temporal features in the case of an ERP -based B C I , the classifie r must be
regularized b y shrinkin g the estimated cov ariance matrix (Tomi oka a nd Müller, 2010 ;
Blankertz et al., 2011 ; Bartz and Müller, 2013 ). The regularization will help preventing
overtraining and is more robust with re spe ct to outliers, due to the reduction of the

Chapter III . Si gnal processing and machine learning methods in BCI research
38
ge n eralization er ror (Jain et al., 2000 ; Duda et al., 2001 ). In addition, classifiers can be
applied for binar y decisions or even more for multi -class discrimination. W hile some of them
needs some tuning in order to be applied , e.g. Logistic Regression (LR) adapt ed to
Multinomial L ogistic R eg ression (ML R) (Böhning, 1992 ; Greene, 2012 ), others can easil y
work in both cases, e.g. LDA. Multi -class classif ication is necessary when aiming to decode
multiple user states, and obtaining faster performance as compared to t he use of multiple
binary dis crimination (Dornhege et al., 2004a ).
3.4.1 Line ar Discrim ina nt A nalysis (LD A)
Linear discriminant analysis (LDA) is a simple classification method due to its linearity ,
facile use and eas y implementation, and a po werful method providing high p erformances. It
is well suited for EEG data, because it starts from the assumptions that: i) the data is Gaussian
distributed; ii) all classes have equal covariances; and iii) the true dist ributions of the class es ,
means µ i , and cova rian ce matri x ∑ , are known. W hile the chara ct eristics of the EEG data
type app roximately fulfills already th e first two points, the true distributions: the means µ i ,
and covariance m atrix ∑ still have to be estimated. Th e decision boun dar y for s eparating
between classes consists of a h yperplane, described b y : w T x + b = y ( x ) with y ( x ) = 0, where
w T is the weight vector that describes the orientation of the h y p erplane and b is the bias
repre s enting the location of the h yperplane. The class belon ging is define d by th e position in
relation to the h y p erplane: negative for one class ( y ( x ) < 0) and positive for the second cl ass
( y ( x ) > 0). Re f erring to two cl asses discrimination, the weight matrix is g iven by 𝐰 =
∑
 −1 (𝜇  2 − 𝜇  1 ) and the bias is given b y : b = 𝐰 T (𝜇  2 + 𝜇  1 )/2 . Moreove r, LDA seeks the linear
projection w that best separates the classes: such as minimizes the within -class variance σ w
while max imizes the vari ance between cl asses σ b , mathematically defined by max imizing the
ratio of the distributions:
max
𝐰 𝜎 b
2
𝜎 w
2 = max
𝐰 (𝐰 T (𝜇
 2 −𝜇
 1 )) 2
𝐰 T (∑
 2 −∑
 1 )𝐰 = max
𝐰 𝐰 T ∑ b 𝐰
𝐰 𝑇 ∑ w 𝐰 (3.17)
with ∑ b a nd ∑ w denotin g the corresponding between-class and within -class co variance. For
multi class discrimination, the between class variabilit y can b e defined b y the cov ariance of
the class means µ : ∑ b = 1 /N C ∑ (𝜇  𝑖 − 𝜇  ) (𝜇  𝑖 − 𝜇  ) T
N C
𝑖 =1 .
3.4.1.1 Regularization with Shrinkage of the Covariance Estimation (rLDA shrink)
As described earlier, re gularization is mandatory in order to avoid ov erfitting. On e common
approac h to reduce the di stortions for the estimated covariance that appear due to the curse o f
dimensionality effect, is to perf orm shrinkage of the covaria n ce matrix ( Friedman, 1989 ).
Therefore, the estimat ed covariance ∑
 is shrunk b y a reg ula rization parameter 𝛾 ∈ [0 , 1] and a
scaling p arameter 𝑣 : ∑
 (𝛾 ) = ( 1 − 𝛾 ) ∑
 + 𝛾𝑣 𝑰 (3.18)
While 𝑣 is computed as the average eigenvalue of the e stimated covariance, the
computation of the optimal reg ula rization parameter 𝛾 requires more effort and is comprised
between zero (no shrin kage) and one (spherical cova riance). Since earlier approaches of
estimating 𝛾 in the cross-validation step is co mputationall y intensive (Frie dm an, 1989 ),
analy ti cal approaches that minim ize the Mean Squared Error are mor e e fficie nt (Ledoit and
Wolf, 2004 ; Schäfer and Strimmer 2005 ).

3.4 Classification and Reg ression
39
For N feature vectors: x 1 , …, x N ∈ ℝ d , Z n = ( x n – 𝜇  )( x n – 𝜇  ) T is d efined for each trial n ,
with 𝜇  = 1
𝑁 ∑ 𝐱 𝑁
𝑁
𝑛=1 , the empirical m ean. The optimal shrinkage p arameter 𝛾 * , can be
analy ti cally computed by:
𝛾 ∗ = 𝑁
(𝑁 −1) 2 ∑ 𝜎 2 𝑛=1 ,..,𝑁 ( Z 𝑛 ) 𝑖,𝑗
𝑑
𝑖,𝑗=1
∑ (( ∑
 − 𝑣𝐈 ) 𝑖,𝑗 ) 2
𝑑
𝑖,𝑗=1 (3.19)
where ∑ 𝑖 ,𝑗
𝑑 is the sum of the entiti es; σ 2 n = 1 ,..., N (Z n ) is the variance of Z n ; ( ∑
 − 𝑣𝐈 ) 𝑖 ,𝑗 represent
the element of (∑
 − 𝑣𝐈) at position row i and column j ; and ∑
 is the standard estimator of the
true covariance matrix ∑, namely the empirical cov ariance matrix: ∑
 =
1
𝑁− 1 ∑ (𝐱 𝑛 – 𝜇  )(𝐱 𝑛 – 𝜇  ) T
𝑁
𝑛=1 .
3.4.1.2 Sliding LDA
An LDA b ased approach useful t o discriminant ERP potentials without alig nin g them, relates
to a sliding window approach. Mainl y, the L D A classifier is trained for a particular tempo ral
interval in sliding manne r. The f eatures are consid ered as different time del ay s from the ons et
of the event. In addition, the method can help for the feature s election pr ocess b y detecting
the most relevant time int erva ls for classification b y estimating the hig hest classifi c ation
performa n ce among all slides.
3.4.1.3 Quadratic Discriminant Analysis (QDA)
The Quadratic Discriminant Anal y sis (QDA) uses quadratic boundaries to separate between
classes (e.g. circle, ellipse, parabola, h yperbola or can also be linear), a s compared to onl y
linear separation perf ormed by LDA. QDA requires also Ga ussian dist r ibuted data as LDA,
except the constra int regarding the equalit y of the class covariance ma trices which is not
required (Hastie et al., 2008 ). Therefore, becau se the class covariance matrices a re not
identical, the covariance matrix Σ k has to be estimated separatel y for e ach class k = 1, ..., N ,
which g iv es quadratic terms in the discriminant function:
𝛿 ( 𝐱 ) = − 1
2 ( log ∑ 𝑘 ) − 1
2 (𝐱 − 𝜇 𝑘 ) 𝑇 ∑ 𝑘 −1 ( 𝐱 − 𝜇 𝑘 ) + log 𝜋 𝑘 (3.20)
Then, the classification rule is similar, finding th e class k that m aximizes the
discriminant function: 𝐺
 ( 𝐱 ) = 𝑎𝑟𝑔 max
𝑘 𝛿 𝑘 ( 𝐱 ) .
Due to its flexibi lity regarding the covariance m atrix, QDA inclines towards a better
estimate of the data as compar ed to L DA, although it’s also more c omplex, with more
parameters to be estimated. Moreover, if the dat a is almost linear ly distributed, QDA mi ght
have higher model variance and so leanin g to overfitting. Moreover, for limi ted data, the
computed covariance matrix of the training data might be inaccurate. Therefore, it might be
better to reduce th e complex it y of th e model in this case and refer to a common covariance
matrix assumption as in LDA.
When c hoosing a classifier model, it is important to s elect the best compromise
between fitti ng the data in a better wa y while having a complex model tha t ca n induce more
errors or using a simpler classifier, but which does not fit the data accuratel y . In any case, a
perfect classifier model is almost impossible to be achieved, esp ecially for complex and
mixed data distributions. In addition, the performance of the classifier on the unseen data

Chapter III . Si gnal processing and machine learning methods in BCI research
40
might be higher in case of a simpler model, because it is more robust to outliers and
variability in the dat a.
3.4.2 Bin omi al L ogistic Regressi on (LR)
In case of a L o gistic regression (LR) model (Cox, 1958 ; L on g 1997 ; Tomioka et al., 2007 ;
Greene, 2012 ), the outcome (the depe nd ent var iable) is categorical, expressed in binary
format (0 and 1). Th e binar y logistic model estimates the percentage that a risk factor affects
the probability of a spe ci fic re sponse.
Considering a set of n o bservations represented by th e vec tors x k , a ggrega t ed in the
data matrix X of size n × k , with y the outcome and ε the vector of dist urbance s, the model
can be describe d b y: 𝐲 = 𝐱 1 𝛽 1 + ⋯ + 𝐱 𝑘 𝛽 𝑘 + 𝜀 , (3.21)
equivalent with the form: 𝐲 = 𝐗𝛽 + 𝜀 .
The goa l is therefore to estimate β . After app ropriate transformation, the linear model
can be expressed in the form: 𝐲 = 𝐀𝐱 𝛽 + 𝑒 𝜀 , which can be unfolded to:
ln 𝐲 = 𝛽 1 + 𝛽 2 ln 𝐱 2 + ⋯ + 𝛽 𝑘 ln 𝐱 k + 𝜀 . (3.22)
For multiple outcome categories, th e discrimination is analyzed b y mul tinomial
logistic regression, desc ri bed in the following subs ection.
3.4.3 M ulti nom ial Logistic R eg re ssion (MLR)
Multinomial L o gistic Reg ression (M L R) extends the binomial Logistic Regre ssion ( L R) b y
predicting a nomi nal dependent variable with more than two categories. The multinomial
logistic function ( Böhning, 1992 ; Greene, 2012 ) that describes the response probabilities of a
nominal model (Bock, 1997 ) in relation to the linear combination o f predictors X β , is
described by:
ln ( 𝜋 𝑖
𝜋 𝑟 ) = 𝛽 𝑖0 + ∑ 𝛽 𝑖𝑗 𝐗 𝑖𝑗
𝑝𝑟
𝑗 =1 (3.23)
with i =1, ..., k -1, wh ere k is the numbe r of categories, pr is the number o f predictors, π is the
categorical proba bilit y and r is t he re ference categor y .
3.4.4 R eg re ssion
Generally, regression describe the relation between the variations of the output and the
predictors (or features). The relation is g enerall y estimated by the condit ional expectation,
expressed b y the average of the output when th e input is fix ed. Various regressi on approaches
can be used, su ch as l inear regression, ridge regression, logistic re g r ession for binar y
classification or multinomial logistic r e g r ession for multi -class discrimination.
As a reference method for more complex reg ression models, we first describe the
linear model in the following .
Having a set of predictors x 1 , … , x N , the goal is to predict the outcome y given a new
input x . Then, the li near model is defined b y : 𝐲  = 𝐰 T 𝐗 and the optimal weight vector w is
computed b y minimizing the loss between the true output y and the prediction 𝐲  . The
commonly used loss function to be minimized is the Lea st-Squa re Error (LSE) method:
min 𝜀 ( 𝐰) = ∑ (𝐲 𝐤 − 𝐰 T 𝐱 k ) 2
N
𝑘 =1 . (3.24)
After derivation and e qu ation calcula tion, w is expressed by:

3.4 Classification and Reg ression
41
𝐰 = (𝐗𝐗 T ) −1 𝐗𝐲 ∝ ∑ −1 𝐗𝐲 .
3.4.4.1 Ridge Regression shrink (RR shrink )
In addition to a linear regression approach which is mainl y similar to LD A (Duda et
al., 2001 ), the re gular i zed version namel y rid g e regression, is better tailored for hi gh
dimensional data, making the model more robust to outliers. The regularization can be
achieve d b y shrinkage of the covariance matri x, similarly to the rLDA shrink method
described in Sec tion 3.4. 1.1 .
Therefore, a regularization term (Hoerl and Kennar, 1970 ) is added to the loss
function: 𝜀 ( 𝐰 ) = (𝐲 − 𝐰 T 𝐗) 2 + 𝜆 ‖ 𝐰 ‖ 2
2 , (3.25)
where the parameter 𝜆 enforce the shrinkage from high values of w , towards zero (no
shrinkage, linear model). The optimal w is computed by :
𝐰 = (∑ + 𝜆𝐈) −1 𝐗𝐲 . (3.26)
3.4.5 Classificati on validation
3.4.5.1 Cross-validation
Considering classification, an important procedure is to estimate the tru e error rate of the
given classifier, especially when an independent set of testing samples is not available, or the
dataset is limited. Therefore, in offline analy si s, a good approach such a s Cross-Validation
(CV) is customar y empl oy ed. The technique evaluates the p redictive valu es of a classifier in
order to anal y ze t he unseen data (Lemm et al., 2011 ). Specifically, the data is split into K dat a
segments also called as folds, of which a bi gger part (usuall y 90%) is used as tr aining data
and one part (10%) is kept as t esting data . Th e CV procedure is appl ied K ti mes while
choosing different se gments for the validation (e.g. ever y segment is us ed ex actly one time as
a test set). The final estimation is computed by averaging the performan ce values for all K
repetitions. I n addition, in order to diminish the variance of the c ross -validation estimator, a
repea t ed CV procedure can be per formed, in whic h every CV is repeated T times b y shufflin g
the entire dataset and dividing a ga in into K folds as described above. The resulted
performa n ces are then average d ov er K folds and T repetitions.
The fe atures in th e t raining set are used for learning the classifier mo del and the
corre spondin g generalized features within the test set ar e used to validate th e classifier.
Note : It is very important that the training features and test features are independent,
such as no information from the test set, especiall y lab el information, has bee n used to
compute the training features or other model paramters ! This is the main charac t eristic of an
unseen test s et. The refore, grea ter precaution h as to be emplo ye d in the feature extraction and
selection processes ( For more details, please see Blankertz et al., 2008c ; Lemm et al., 2011 ;
Haufe at al., 2014a ).
3.4.6 C lassification eva luation
Assessing the performa nce (accurac y ) of a BC I s y stem is also an imp ortant process and
requires ca reful implementation and investigation, not to incorrectly estimate sy stem’s
performa n ce. Generall y , it indicates the final decision of the system that relates in one wa y or

Chapter III . Si gnal processing and machine learning methods in B C I research
42
another to the percentage of correctly classified epochs. A commonly used measure for
binary class evaluation perf orman ce is r epresented by the Area Under the ROC Curve
(AUC), while for multi-class estimation normalized loss can be used, for example. These
measures a lon g with some other important procedures are described in the following.
3.4.6.1 Area Under the ROC Curve (AUC)
The Ar ea Under the ROC Curve ( AUC) (Hanle y and McNeil, 1982 ) is a good common
measure for estimating bin ary classification performance, compute d based on the Receiver-
Operator Characteristics (ROC) curve (Green and Swets, 1966 ). The R OC curve is a
graphica l representation expressing the “True Positive R ate (TPR)” also named “sensitivity” ,
in relation to th e “false p ositive rate” also kn own as “1 - sp ecificity”. The ar ea under th e ROC
curve (AUC) can be view ed as a generalization of the ROC curve to a single rational value .
The AUC measure ranges between 0 and 1 , where a v alue ov er 0.5 is t arge ted for binar y
discrimination, re presenting a good performance better than chance level, undoubtedly
considering the a pprop riate statistical signific an c e measures (Appendix A.1.1 ).
3.4.6.2 Normalized loss
In order to assess the accurac y of a multi -class classification, when the AUC measure cannot
be applied an y more, the method to be referred to has to include no rmalization. Additionall y ,
the class-wise normalized loss helps with weighting in case of unbalanced classes:
𝑙𝑜𝑠𝑠 = 1
𝑛 𝑐𝑙𝑠 ∑ 𝑁 𝑒𝑟𝑟_𝐶 𝑖
𝑁 𝐶 𝑖
𝑛 𝑐𝑙𝑠
𝑖 =1 , (3.27)
where n cls – the number of classes ; 𝑁 𝑒𝑟𝑟 _𝐶 𝑖 – the number of wr on g l y estimated samples in
class C i ; 𝑁 𝐶 𝑖 – the number of samples in class C i . W hile this result represents the loss ratio
out of 1, the a ccuracy is complementa r y to the loss , given b y : A cc = 1 – loss . When cross-
validation is applied, the normalized loss is computed insi de folds, and then averaged over all
folds.
3.4.6.3 Confusion matrices
The classific ation accurac y v alue can be mi sleading in case of dif fere nt numbe r of
observations between classes, or for multi -class discrimination. I n this sense, computing
confusion matrices is a useful approach, whi ch provides a detailed overview of the
classification errors and perf ormance. To put it simply , a confusion matrix (Kohavi and
Provost, 1998 ) is a table layout that describes the amount of corr ect and mislabeled
(confounde d ) samples fo r each class. The columns refer to the p redicted class instances and
the rows show the true class instances, showing the correct classified samples in each class on
the diag on al.
For unbalance d classes, normalized confusion matrices have to be perform ed. For a
larger overview of the classification performance , confusion matrices can be represented for
single participants, or as grand a ve rage over all participants.
The amount of errors and the t y p e of misclassifications that are described b y the
confusion matrix, arise from the resubstitution.

3.5 BCI applica tions
43
1. Resubstitution error and accuracy
The resubstitution error shows the misclassification costs , namely the difference
between the response given b y the classifier on the trainin g data and the p redictions responses
performe d on the test data with the information from the training data. A high value of the
resubstitution error, will impl y a weak performance of the classifier and a poor prediction of a
new dat a. In an y case, the opposi te is not va lid: a small resubstit ution error does not
necessarily impl y a good fit on new d ata. How ever, it sti ll gives a good overview of the
estimation and the performance of the classifier.
An accuracy measure, Acc, can be computed using the general confusion m atrix, R , of
the classifier tha t r elates to the resubstitution errors:
𝐴𝑐𝑐 = ∑ 𝐑 ii
k
i=1
∑∑ 𝐑 ij
k
j=1
k
i=1 , (3.28)
where k r epresents the n umber of classes and R ij is the number o f observations of class i ,
estimated as class j .
3.4.6.4 Statistical testing
In order to reinfo rce th e performance o f a classi fier, it is crucial to test the statistical
significa n ce of the model outcome. I n addition, for example when interpreting and correlating
differe n ces and similarities in the ERPs among tria ls and participants, or for extracting
inferences over the data , statistical anal y sis is mandator y to be perfor med. The statistical
evidence assures if the inferences conducted on the diverse sample data are valid (not due to
chance) and can be deducted in genera l fr om a larger data. Amon g the wide range of
statistical tests (Fukuna ga, 1990 , 2013 ), the com mon tests can be classified in the following
categorie s: de s criptive statis tics (e.g . no rmality t est, correlation coefficient measure, mea ns
comparison tests ), h ypothesis testing (e.g. t-test, chi-square d test), ana l ysis of vari ance ( e.g.
ANOVA , F-test, chi-sq uared test for variance, Barlett test ), multi ple comparisons (e.g.
Bonferroni correction) , and non-parametric testing (e.g. W ilcoxon rank test, Kolmogorov -
Smirnov test ). The choice of the statistical test depends on the ex periment design, the t ype of
variables in the data se t, and the distribution of the data. Considering data distribution for
example, parametric tests are suitable in case of normally distributed data, while on the other
hand, non-parametric te st s can be a pplied to non -normal data.
For data visualization purposes regarding the statistical distributi on of the data, statistical
charts such as histo grams and box charts are usua lly represented. S hortl y , the main statistical
tests used in this thesis are described in Appendix A.1.1 .
3.5 BCI applications
A brief ov erview ov er ex isting BCI paradigms and applications is descri bed in this section ,
mainly focusin g on those relevant for the conte xt of this thesis. As follows, the common
ERP-based BCI paradigms using visual and auditory stimul i are described. Further, motor-
imagery based BCIs are briefl y discussed, followed by th e general research and applications
on mental state detection. The possible BCI applications range from entert ainment (Krepki et
al., 2007 ; Lécuyer et al., 2008 ) to control o r co mmunication (Vida l, 1973 ; Dornhege et al.,

Chapter III . Si gnal processing and machine learning methods in BCI research
44
2007 ; Wolpaw and Wolpaw, 2012 ). Whil e initiall y , the BCI technologies have been
developed b y th e resea rch community as assis tive devices for patient s with disabilities
(Pfurtscheller and Neuper, 2001 ; Neuper et al., 2 003 ; Kübler et al., 2005 ; Birbaumer et al.,
2008 ; Mak and Wolpaw 2009 ), the purpos e of BC I research has expanded also towards non-
medical applications for health y individuals (M üller et al., 2008 ; Blankertz et al., 2010c ,
2016 ; Zander and Kothe , 2011 ; van Erp et al., 2 012 ; Allison et al., 2012 ; Gamberini et al.,
2015 ). Primarily initi ated in controlled laboratory settings, BCIs developed to the extent of
home use, industrial environmental settings (Ve nt hur et al., 2010 ), or even outdoor activities
such as driving (Haufe et al., 2011 ; 2014c ), air traffic control (A ricò et al., 2016a , b ), piloti ng
(Borg hini et al., 2014 ).
Moreover, the range of possible BCI applications is even wider (Moore , 2003 ), and
the type and context of applications will cer tainl y expand in the following y ears .
3.5.1 Applicat ions for ERP-based par adigms
Starting from the vit al ERP-based BCI p aradigm with visual stimuli develop ed for the
purpose of communication, namely the matrix speller by Farwell and Donchin ( 1988 ), more
and more complex and diverse paradi gms have been d eveloped for differe nt purposes.
Controlling BCIs through ERPs ( Vidal, 1973 ; Blankertz et al., 2011 ) is a vast well-known
approac h, re l ying on different ERP components like P300 (F a rwell and Donchin, 1988 ;
Treder and B lankertz, 2010 ), or on some other subt y pes of ERP s, such as visuall y evoke d
potentials (VEP) (Müller-Putz et al., 2005 ), or auditory evoked potentials (AEP) (Schreuder
et al., 2010 ; Höhne et al., 2011a ). The applic ations rang e f rom controlling a computer
application or a virtual e nvironment and games ( Ba y li ss and Ballard, 20 00 ; Lé cuyer et al.,
2008 ) towards more artistic use, such as brain painting (Kubler et al., 2008 ).
3.5.2 Motor-imager y based BC Is applications
The applica tions based on motor-imagery BCIs includ e motor control ( Pfurtscheller et a l.,
2003 ), communication (Kübler et al., 2005 ; Blankertz et al., 2007 , 2008b ), controlling
computer applications ( Wolpaw et al., 1991 ), robots, prosthesis, wheelchairs (Vanacker et al.,
2007 ; Ga lán et al., 2008 ) and other electronic devices. The respective applic ation or device
can be controlled b y t he BCI users throu gh self-induced amplit ude variations of th e
sensorimotor rhythms ( SMRs) (W olpaw et al., 2002 ; Wolpaw and McFarland, 2004 ;
Blankertz et al., 2007 ).
3.5.3 Mental state BC Is applicati ons
An emerging p aradigm n owadays relate s to the investigation of the ongoing EEG activit y , b y
detecting user’s mental state and intention s for the benefit of humans. Using a non-control
BCI interaction based on “implicit information” obtained f rom the neuroph y siological
activity, which does not requir es a direct user int eraction (Kohlmorgen et al., 2007 ; Müller et
al., 2008 ; Blankertz et al., 2010c , 2016 ; Allison et al., 2012 , Zander et al., 2014 ; Brunner et
al., 2015 ; Schultze-Kraft et a l. , 2016b ; Na um ann et al. , 2017 ), e x ceeds the possibilities
offered b y “e x plicit control” available within co ntrol-BCI s y stems (Birbaumer et al., 1999 ,
Blankertz et al., 2007 ; Wolpaw and W olpaw, 2012 ). Specificall y , different BC I designs have
been used to monitor and investigate the user co gnitive mental state, represented b y attention,

3.5 BCI applica tions
45
workload, stress, t ask engagement, decision-ma king (Klimesch, 1999 ; Kohlmorgen et al.,
2007 ; Müller et al., 200 8 ; Ve nthur et al., 2010 ; Borghini et al., 2014 ; H aufe et al., 2014c ;
Gamberini et al., 2015 ; Schultze-Kraft et al., 20 15 ), and man y other fac tors, for example
investigating the brain activit y r elated to music (Make i g et al., 2011 ; Treder et al., 2014 ;
Sturm et al., 2015 ; Vaid and Singh, 2015 ). Th is t y pe of B C I s that moni tors and int erprets
user ’s state and learn to adapt the int erface according to user ´ s cognitive and affective state,
without the restriction of conscious user control a nd continuous interaction with the s ystem,
are termed p assive BCI s. This approach brin gs noti cea ble advanta g es to the BC I interaction,
compared to the BCI co ntrol based on volunt ary brain a ctivit y modulati ons which re quire
considerable amount of time for user training in order to learn how to modi fy th eir brain
activity a nd additional time to obtain good performances.
Considering this fac t, it can be ex tended to adapt information seeking applications
(Nicolae et al., 2017a ), industrial operator monitoring s y stems (Venthur et al., 2010 ) and
many others (Müller et a l., 2008 ; Blankertz et al., 2010c , 2016 ; Za nd er and Kothe, 2011 ; Erp
et al., 2012 ).
Although online menta l state BCI applications are still in the resea rch and
development step, the advancement of powerful machine learning techniques will materialize
them into practice (Blankertz et al., 2002 ; Krauledat et al., 2004 ; Müller et al., 2004 , 2008 ).

Chapter III . Si gnal processing and machine learning methods in BCI research
46

47

Chapter 4

Revealing the neu ral correl ates of
user efficient motor imagery tas k s

As describe d in the introduction of this thesis, it is important that for the development of
improved Brain-Computer Interfaces, the int erest should be focused on th e user. This study
investigates dif ferent stimuli applied in a s y nchronous BCI system, to determine the most
effective ones by anal y zi ng the use r reaction time during real motor movements and the brain
activity during motor im ager y mov ements, under the assumption that some BC I stimuli may
ge n erate faster reactions and stron ger cortical potentials than others. Vi sual and auditor y
stimuli were chosen for investigation, and the c orresponding brain potenti als were further
compared. I n addition, different e fficient moto r imag er y tasks that attracts users’ attention
and interest, while targeting stronger brain activit y and fast and accurate mental movement
execution for a llowin g a fa cile and agile BCI interaction. These user efficient tasks could
provide a new mental co ntrol strategy for futu re BCI applications. As an acquisition method
for brain signal recording the Electroencepha lo graphy (E E G) method is used in this stud y .
4.1 Introduction and state of the art
Motor imager y -b ased Brain-Computer I nterfaces ( BCIs) are the m ost lar gel y
investigated sy st ems in BC I research (Graimann et al., 2010 ) . The BC I d ecisions are founded
on two or more motor imagery tasks, which ca n be expressed b y th e mental practice of
differe nt motor actions without the use of muscles or any bod y part activit y , but onl y the
brain. Aiming at improving and supplementing daily life activities, the BCI applications
involve communication or control purposes (W olpaw et al., 2002 ), or even rehabilitation
(Mokienko et al., 2014 ). The implementation is p ossible by th e replacement, enhancement or
the repa irment of motor or cognitive functions, for example controlling a prosthesis or a
wheelchair device via b rain signals for the individuals who lost a body p art in an accident

Chapter IV. Revealing the ne ural correlates of use r eff ici ent motor imager y tasks
48
(Kübler et al., 2006 ; Vanacker et al., 2007 ; Galán et al., 2008 ), or rehabilitating the capabilit y
to ex ecute a real mot or movement using motor imager y tr aining for patients after stroke with
small im pairment or severe paresis (Mokienko et a l., 2014 ; Morone et al., 2015 ; Carrasco and
Cantalapiedra , 2016 ).
As described in the Fundamentals Chapter ( 2.4.2. 2 ), the neural si gnals that are used to
drive a motor imager y BC I s y st em, generally i nvolving hand and/or foot movements, are
character iz ed b y a decrease in the mu band power (8 -13 Hz), namel y des y nchronization,
appearing in the contra l atera l sensorimotor cort ex, followe d b y a s y nchronization in the
ipsilateral cortex (Pfur tscheller and A ranibar, 1977 ; Pfur tscheller and Neuper, 1997 , 2001 ;
Pfurtscheller a nd Sil va, 1999 ; Pfurtscheller et al., 2006 ).
Despite the tremendous research in the last de cades over the motor im agery BC I
systems, some shortcom ings still ex ist for this approach and this feasibili ty stud y aims to
diminish the effect of one of them, namely the bo thersome and tiredness effects on th e use rs
when performing the same tedious motor imagery motion for several r epetitions (Pomer-
Escher et a l., 2014 ; Tre jo et al., 2015 ; Ta lukdar and Hazarika, 2016 ). By choosin g proper
stimuli, efficient tasks and captivating paradigms, the users can gain interest in using the BCI
and his performance and similarly the B C I’s performance could increase over time.
Targeting this goal, diff erent efficient motor imagery task s are investigated in this
study and one t ype particularl y relates to us er’ s i ntere st and hobb y . After the completion of
the stud y , th e user expressed strong int ere st in be nefiting from such a BCI system. Th e stud y
investigat es the oscillator y activit y considering the Sensori -motor rh y thms (SMRs) observed
as spectral p erturbations (Makeig, 1993 ) which generat es (de)s y chnroniz ations in the mu
band, consistent with the scientific literature. The efficie n cy o f the proposed tasks was further
demonstrated by an in creased classification performance of the neural activity. Ove rall, we
recommend usin g pre-defined efficient mot or im agery tasks for r ehabilitation, cognitive
functions enhancement, or any other possibl e motor imagery based BC I application.
Specifically, finger button press, arm li fting and a user defined trigger pulling activit y in form
of imagery movements are investigated, considering visual or auditory stimuli in a BCI
paradigm. While the motor image r y tasks that we use as a bas is for comparison, namely
finger movements (B lankertz et al., 2006a ; Stavrinou, 2007 ; Furman et al., 2016 ; Ka plan et
al., 2016 ) and arm movements (Badia et al., 2013 ; Tavakolan et al., 2016 ) receive a lot of
attention in the scientific community , no use r-defined imager y tasks are investigated to the
best of our knowledge, making this investi gation a novel conce pt in the BCI community.
The visual and auditory stimuli of the BC I s y stem, were carefull y chosen to improve
the reaction time of the BC I use r and enhance its brain activit y , envisaging the limitations and
capabilities of the visual and auditor y human system. Different visual and auditor y stimuli
types were tested via the interface with a real mo vement execution experi ment given b y the
easier task of finger but ton press. The most efficient stimuli were selected for the motor
imagery experiment b y investigating the P SNR of the signa ls and the user reaction time.
While this eva luation of the stimuli in re al motor execution experiments is not presented in
this thesis, detailed information and re sults are presented in Nicolae, ( 2013 ).
In terms of investigation and methods approach (S ection 4.2.3 ), the selection emerged
from the following. Generall y , th e motor im ager y brain r esponse i s mostl y an aly zed
considering the spectral domain (Lee et al., 2009 ; Lee et al., 2010 ), which onl y considers one

4.2 Methods
49
aspect of information encoded in the neural dat a. The temporal informatio n, ERP s, could also
provide intrinsic knowledge of the a ctivity within the patterns. While ERP alone can not
entirely discole the brai n response characteristics as response to ex ternal stimuli (Makeig,
1993 ), the ERSP measure ma y offer information that is not contained in t he ERP , through it s
investigation over the power modul ations changes (M akeig et al., 2004 ). Therefore,
combining the time and frequency domains seem a reasonable approach t o be considered in
order to provide additional complementar y infor mation which could highly provide not onl y
in -depth understandin g of the neural activit y , but also advantageous classification outcome
(Makeig et al., 2004 ).
4.2 Methods
4.2.1 Exper imental design and s cenar io s for efficient user motor imagery
tasks
4.2.1.1 Participants
The experiment is a preliminary case study p erformed on a male participant of 27 ye ars old ,
with no experience in BC I. The participant is right-handed and has no rmal eyesight and
normal hearing. The participant gave his written informed consent regarding the invol vement
in th e study and the perm ission to record his brain sig nals and behavioral measures durin g the
experiment for research purposes. The ex periments were performed du ring evening, in a
laboratory environment, with 35 dB backg round noise. For the mom ent, only one participant
was chosen in ord er to i nvestigate the impact of the efficient tasks and t o adapt the signal
processing and m achine learning methods for an enhanced discrimination of the motor tasks .
After the feasibilit y o f e fficient motor imager y tasks is an alyzed and d emonstrated, the n ext
future stud y will include an adequate statistical number of pa rticip ants.
4.2.1.2 Experimental paradigm
4.2.1.2.1 Stimuli
The ti ming and structure of the ex perimental desig n is detailed in Fig. 4.1 , and is
composed of thre e periods: i) relaxation, ii) attention and iii) trial. The first relaxation
segment lasts for 2s and is represe nted b y a black screen. The n ext attention segment which
lasts for 0.5s, prepares t he participant for the actual trial by pre senting a cue with the “ +”
symbol of a 218 × 218 pixels siz e for the visual st imuli experiment and by pla y ing a beep
sound for the auditory stimul i experiment. The motor image r y task is performed in the trial
segment of 3s lon g and i s triggered b y a left/ri ght arrow s ymbol of siz e 392 × 214 in case of
visual stimulation, or the pronunciation of the left/right word b y a female voice of 250 Hz
freque n c y with 70dB in t he auditory (voice) stimul i experiment. The visual stimuli are scaled
1 to 3 as compared t o the total screen size and the format used is PNG. For auditory stimuli,
the format used is WAV.
Shortly, three stimuli sc enarios we re investi gated (as in Tab. 4.1 ), considering
differe nt sti muli ty pes : 1) Visual S timuli ( Vis. St .) referring to the use of vi sual arrows for th e
task and visual cue for the att entional period; 2) Visual Sti muli with an auditory attention cue

Chapter IV. Revealing the ne ural correlates of use r eff ici ent motor imagery tasks
50
repre s ented b y a di g ital Sound ( Vis.-So. St. ); 3) Auditor y Sti muli and auditory cue given by
Voice Stimuli for the task and a digital sound for the attentional cue ( Vo. S t. ).
The ef fec tive visual stimulus related to color w as sele cted from the real left/right
finger button press experiments by anal y zing the results regarding us er acc u rac y rate and
reac tion time. Although the efficient and simple visual sti muli that trigge r ed the faster
response considering real button press movement was the green stimuli on white background
(Nicolae, 2013 ), we dec ided to select white stimuli on black scre en for motor imager y , in
order to have neutr al stimuli for the ge neral use (e. g. regarding color blindness). For the
auditory attentional cue, the sound was selected with 3300Hz, 75 kbps and 80 dB amplitude.
The participant sta y ed se ated with 50cm in front of the L CD screen and wa s requested
to focus in the center of the screen and to complete a randomized series of 40 trials fo r ea ch
experiment: 20 trials for each l eft or ri g ht mov ement. The r educed nu mber of trials was
imp osed in order to test the feasibility of usin g, as possible, a re du ced number of task
repetitions for a BCI s ystem that will increa se the interaction speed, reduce the tiresome
effect at the use r side, while still obtaining a good s y stem perform ance in terms of
classification.
The stimuli presentation was shown on a 22 " L C D displa y (HP LE2201w) with 60 Hz
freque n c y rate and built-in speakers. The software used for presenting the sti muli is S uperL ab
4.0 ( Cedrus Corporation ). For the button press r esponse, the RB-730 Resp onse pad was used
connected with the computer via USB cable.

4.2.1.2.2 Mental tasks
Aiming a stronger activation in the mot or cortex (Guil lot et al., 2009 ), kinesthetic
motions from first person perspec tive are performed in the experiments, which consist in
mental ex ecution of all the muscle contractions and actions required by the respective
movement, impl y in g th erefore a much more complex process than a vis ual im ag ination of the
movement. The participant was trained thre e da ys in advance and co uple o f times in the da y
of the experiment in order to pra ctice and get familiarized with the motor imager y concept.
The neurofeedback sho wn for this motor im agery tra inin g phase was repre s ented b y the
ongoing EEG si g nals as an amplitude evolution in time. During the experiment, different
motor imagery mental tasks are inspected which involve left/right arm and finger movements,
namely : index finge r button press (index flexion), arm lifting, and a specific task related to
user background and h obbies. For the arm lifting task, the user was asked to imag in e the
movement as if he would tr y to reach the presented visual stimul us. Considering the spe cific

Fig. 4.1 Timing for one expe rimental trial (with t=0 the stimulus onset) composed by
the relaxation time (2s ), the attention cue (0.5s), and the actual task period (3s) with
right movement execution in this example. Same timing is considered for the auditory
stimuli, where the digital sound is presented for 500ms in place of the attentional cue.
(Figure taken from Nicolae, 2013 , with permission)

4.2 Methods
51
task, t he user freely selected the mental strat egy that he is most comfortable with, and he
chose ‘gun tri gge r pulling’, b ased on his main hobby and ex perienc e in shooting games. This
involves an imag er y left or right arm li fting as if the user holds a nd raises a virtual gun as in a
ga min g scenario, followed by an imager y index finger flex ion repre sentin g pull ing the trigger
of the g u n.
Considering the auditor y experiments with voice stimuli, the participant concentrated
on the voice spe aking the current tasks, with e yes closed, and pe rforme d the cor responding
imagery movem ents. I n total, ten ex periments are performed considerin g a mixture of stimul i
and mental tasks, as in the table below (Tab. 4.1 ). Further, for the p urpose of a viable
comparison betw een dif ferent experiment t y pes , we choose to pe rform th e anal y sis (section
4.3) on onl y on e run (fi rst ex periment) for each task, in order to have an equal number o f
trials.
At the end of the ex perimental stud y , the participant completed a qu estionnaire
regarding his interest in the experiments and provided a person al estimation of his
performa n ce considering the stimuli t y pes and the imagery motor tasks.
Tab. 4.1 Motor imagery experiment types (Table taken from Nicolae et al., 2014b , w ith
permission)

Stimulus type

Presented
Stimuli

Attention
type

Mental task

No. of
experiments

Visual ( Vis. St. )

white and
black stimuli

white cue

button press

4

Visual and sound
( Vis.-So. St. )

white and
black stimuli

1200 Hz
sound

button press

2

Visual and sound
( Vis.-So. St. )

white and
black stimuli

1200 Hz
sound

arm lifting

1

Auditory ( Vo. St. )

female voice
stimuli

1200 Hz
sound

button press

2

Auditory ( Vo. St. )

female voice
stimuli

1200 Hz
sound

trigger pull

1

4.2.2 Bra in signal acquisitio n and equipment
The EEG activity was recorded usin g the B I OPA C system (BIOPAC S ystems, Inc.), with a
1000 Hz sampling freq uency and amplified w ith 20000 Gain. T he BIOPAC hardware
acquisition s y stem consi sts in the MP150 a cquisition unit, the U IM100C amplifier module,
two EEG100C amplifiers necessar y to record signals from the two hemispheres and the
electrode cap CAP100C. The C3 and C4 ge l-b ased electrodes were used, relating to the
specific brain areas corresponding to the left and right arm movement. I n addition,
Electromyogra ph y (EMG) was also recorded fo r real movements dete ction, but this setting
and anal y sis is not presented here (For more details, please see Nicolae et al., 2014a and
shortly in Appe ndix A.2.2 ). As software acquisition system, Acqknowledge 4.0 was used .
The rec o rded data are transmitted from the ac qu isition s y stem to the comput er via an UTP
Intel (R) 82567M -3 Gi gabit Ethernet network of 100 Mbps speed. The connection between
the BIOPAC equipme nt and the software stimuli s y stem is done via an I/O address bus.
For brain si gna l processing and data an aly sis, the MATLAB (The MathWorks,
Natick, MA, USA ) analysis software was us ed, including the M A TLAB Signal P rocessing

Chapter IV. Revealing the ne ural correlates of use r eff ici ent motor imagery tasks
52
Toolbox, the S tatistics and Machine Learning Tooolbox and the Wavelet Toolbox .
Additionally, the EE GLAB (Makeig et al., 2000 ; Delorme and Makeig, 2004 ) toolbox was
used regarding the time-frequency analysis domain.
4.2.3 P ro ce ssing strategy to detect the specific motor i m agery neural
correlate s
4.2.3.1 Preprocessing
First, for removin g the i nterfering f requencies, a sequenc e of online hardware filtering w as
applied composed b y a 0.1 Hz high pass filter and a 35 Hz 4 pole low p ass Besselworth filter
with a 50 Hz notch filter (40 dB attenuation). Ne xt, t he recorded data were off-line analyzed
in MAT L AB and EEGLAB. Fo r removing the remaining noise, two filtering approaches
were chosen and tested after c areful investigation of the ma gnitude and spectrum response of
differe nt filters : 1) a combination of temporal filters and 2) wavel et filtering. The qualit y of
the signals w as analyzed b y the Peak Signal- to -Noise Ratio (PSNR) measure (Appendix
A.1.2 ) and the best method was sele ct ed thereafter for the processin g pipeline.
1) The temporal filters approach consists in a sequence of an IIR comb filter of order
20 with 0.5 Hz bandwidth and 1 dB magnitude limi ts, applied twice on the signals. Nex t, it is
followed b y a F I R band stop equiripple filter of order 50 with the following constraints: 49.5
Hz – 50 Hz pass b and, 50 – 50.5 stop b and and 5 0.5 – 51 pass band intervals with and 0.5 to
40 dB magnitude p ass f or the frequencies lower than the pass b and inter val and 1 to 40 d B
magnitude pass for higher f requencies. This sch ema is desi gned to filter the ji tters and the
noise line freque n cy of 50 Hz.
2) Th e second filte ring approach employs the Interval-de p endent wav elet denoising
method (Appendix A.1.3.1.5 ), using Daubechies mother wavelet of level 5 with 3 intervals
(Fig. A.1.1 ).
After filtering, the epochs were segmented in t he int erva l (-2.5; 3 s) with baseline
correction (-2.5; 0 s) and the noisy trials were manually remov ed fr om the data by visual
inspection. Additionall y , the artifact rejection technique, namely the I nd ependent Component
Analy sis (ICA) usin g th e Infomax approach (Se ction 3.2.2.2.1 ) was appl ied on the data, in
order to detect the relev ant neural patterns and check the a ctivit y sources (and remove th e
noisy components in case of an im proper aquisition ). The components selection was
manually performed by visual inspection considering the sp atial dist ribution ( power
topogra p hic maps), pow er spe ctrum and components temporal activity among t rials. The
components related to channel pop a rtifacts ( when channe l goes off or the electrode drops or
ge ts loose ) are int ented to be discarded from the data. Finally, no components were removed
by I CA, relating to no noisy electrodes within the available spatial resolution (two channe ls ).
4.2.3.2 Neural correlates detec ti on
In order to analyze the corresponding oscillator y activit y revealing the sensorimotor
sync h ronization or desynchronization, the ERS P time-frequency transform ation w as applied
on the data after the ICA decomposition. The ERSP method (Section 3.3.4.1 ) was computed
using different transformation: Short-Time Fourier Transform (Appendix A.1.3.4.2 ) and
wavelet tr ansform (Appendix A.1.3.4.3 ). The t ransfor mation based on Fourier anal y sis

4.3 Findings
53
considers a Hamming window of 128 samples a nd 100 overlay s, over the spectrum rang e
from 3 to 35 Hz. The ERSP based on wavelet transform considers sinusoidal Morlet wavelets
of 3-c y cles which are increased slowl y with frequenc y , reaching half the number of c y cles as
in the equivalent FFT window of the highest frequenc y (applied with a Hanning overlapping
window).
Spectral pertu rbations are ex pected to appear in the mu frequenc y band ( 8-12 Hz) and
possibl y in beta (13 -30 Hz ), relating to the Event R elated (De)Synchronization (ERD/ERS)
variations (Chapter 2.4.2.2 ). I n addition, the int er-trial variability of the tri als is inspected b y
analy zin g the loca l ph ase coherence with the Inter-Trial Coherence method (Section 3.3.4.2 ).
For the classification, regular ized Linear Discriminant Analysis (r L D A) with
shrinkage of the covariance matrix wa s applied ( Appendix A.1.4 ) in fo rm of a multi-class
discrimination, dec idin g betwe en left, right and no movement. Aimin g a robust and efficient
classifier, the classifier is fitted by s etting the optimal parameters according to the distribution
of each class and the redundant features are remov ed . The best re g ularizat ions para met ers ( γ
and δ ) were explored wi thin 25 levels and detected by considering the lo west error rate of
crossvalidation. The redundant fea tur es as detected b y the two parameters a r e further
removed (as in Appendi x A.1.4 ). The classifier considers mul ti-modal features considering
the temporal, spatial and spectral domains. The amplit ude values for each trial in the 300 -
2300 ms interval withi n the 1 - 49 Hz frequency range are considered for the temporal
features (filtere d usin g 1 Hz FI R high pass filter of order 30 and a Cheb y shev t y pe II of ord er
10 with 3 dB of ripple in the passband up to 42 Hz and 50 dB of attenua tion in the stopband
starting at 49 Hz). The t emporal features are con catenated with the power spectra l features of
the corresponding mu and bet a frequenc y bands (8 -12 Hz ; 18-25 Hz ) where the strongest
perturbations appear, and both features t y pes relate to the two channels (C3 and C4) as spatial
distribution information. The spectral features are estimated b y th e Fast Fourier Transform
(FFT) method and computed on the same time int erva l, 300-2300ms. Fo r the no movement
condition, the temporal and spectral features as amplitude and power values are considered
fr om the relaxation int er val before each trial : from -2500ms to -500ms. The feature t ypes are
normalized using z -score normalization and concatenated in the f eature vector which is given
to the classifier.
The classifier was validated using 5 folds cross-va lidation and the performance was
assessed in t erms of accuracy given b y th e normalized confusion matrix and the
resubstitution errors (Section 3.4.6.3 ).
4.3 Findings
4.3.1 Behavioral data
Information over the behavioral data is offered by the subjective question naire, in which the
participant stated an increased int erest, performance and attention for the voice stimuli and
for the trigger pull task. As regarding subjective performance, the partic i pant ex pressed no
errors regarding a mistaken trial between left or rig ht and no trial misses .

Chapter IV. Revealing the ne ural correlates of use r eff ici ent motor imager y tasks
54
4.3.2 F il terin g measures com parison
When comparing th e temporal and wavelet fi ltering r esponses r egarding the PSNR, a
significa nt bette r qualit y of the signal is observed for the wavelet filtering approach. The C3
and C4 electrode signals reg arding r eal movem ent button presses are considered for the
comparative anal y sis and the wavelet filtering r esults in a PSNR of almost 5 times higher
than in the FI R filtering (Tab. 4.2 ), e f fect whic h is significant with the one- w ay ANOVA
statistical test (p = 0.0052).
Tab. 4.2 Signal quality estimation after filtering, based on the PSNR measure (Table taken
from Nicolae et al., 2014b , with permission)

Signal

Moveme nt

PSNR

FIR filter in g

Wavelet filtering

Channel C4

Left button press

10.7859

51.1709

Channel C3

Right button press

6.0897

55.6603

The result of the noise removal over the quality of the signal is invest igated also
considering the power spectrum. The power s pectral d ensity (PSD) of the signal was
estimated using the W elch method ( Appendix A.1.3.3.2 ) with 1024 Kaiser windows o f 256
size leng th and no overlap. Fi g . 4.2 shows the PSD of th e C3 signal before and after temporal
filtering, while the spect rum for the C4 signa l coincides

2

. We can observ e a cleaner si gna l,
with no stron g peaks at 50 Hz and abov e as interferences from the 50 Hz power line source,
compared to the original signal. The power spe ctrum from 0 to 100 Hz c orresponding to the
brain activity , follows the 1/f shape and contains the 10 Hz alpha peak.

Fig. 4.2 Power Spectral Density E stimate using the Welch method for the original signal ( left
plot) and filtered signal (right plot ). (Figure tak en from [ 14 ] , wit h permission) .
For further anal y sis, the wave let filtering was applied as preprocessin g step due to its
improvement in terms of PSNR .
4.3.3 D etecti n g the neural corre lates of spec ific motor im ag ery t ask s
4.3.3.1 Temporal analysis
First ly , we have a look o ver the evolution of the amplitude modul ations over time in the mu
band, corresponding to the imagery movement. After corresponding band - pass filtering in the
8-12 Hz range, the signals can be further investigated. For example, in case of the arm lifting

2

In ord er to investigate the filter s effects for line noise di minuation ( including t he 5 0Hz line noise echos:
100Hz, 1 50Hz, etc.) , the entire frequency r ange was a nalyzed as the purpose of c omparison (0 -500Hz), while
later on in the anal ysis the pass -band filtered signa l of 0-50Hz was considered.

4.3 Findings
55
task (Fi g. 4.3 ) as response to visual stimuli and auditory attentional cue ( Vis.-So. St. ), after
the 300 ms point which relates to the timin g p erception o f the stimulus , the modul ations
change on average, with stronger fluctuations (higher amplitude) for the left imagery
movement as compare d t o a decreased amplitude for the right imager y mo vement. The lag in
phase s y nchronicit y between hemisphe res and condit ions is more clearl y observed on sin gle
trial (rig ht ima ge in Fig. 4.3 and further in Fig. 4.5 )

Fig. 4.3 Mean amplitude evolution over time for the arm li fting motor imagery task as
response to Vis.-So. St (left image) and single trial amplitude representa tion (right image) .
The thicker line on top of the single trial representation highlights the amplitude profile
(enve lop e) and it was estimated with H ilbert transform. (Left f igure fr om Nicolae et al.,
2016b , with permission)
4.3.3. 2 Ti me-frequency analy sis
Seco ndly , the spectra l perturba tion s ge nerated b y the sensori- motor rhy thm (SMR) are
inves tigate d v ia t ime -f reque ncy repre sentat ions. The Event Rela ted Spectral Per turbat ions
(ERS P) pha se loc ked t o th e eve nt are pre sented for e xample in ca se of the motor imagery
trigg er pul l task as rea ction to voic e stim uli in Fig. 4.4 . Rela ted to the mo tor imagery
moveme nt, the most sig nifica nt pron ounced pertur batio ns accor ding to a 0.01 boot strap
sign ificanc e level ap pear in the mu 8-1 3 Hz freque ncy band in for m of a desy nchroni zation (- 4
dB) for the 500 – 1500m s tempora l interva l con sider ing the c ontra lateral he misphere
(elect rode C3 for righ t movemen t an d elec trode C4 for left movemen t). Th is is fo llowed by a
sy nchroniza tion (4 dB) in the sa me fre quency band for the 1 6 00 – 21 00 ms interva l. Simi lar
follow up wit h a delay appears in the beta band, 18-25H z, star ting with a des y nchroni zation
and is fol lowed b y a sy n chron iza tion. The ERSP also shows a brief but sig nifican t incre ase in
power at about 30H z. Co nsider ing the ipsila tera l he misphere , interes tingly ERD appear s in the
mu and beta ban d. Anoth er inc rease in powe r is observe d a t 500-10 00ms i n the theta range .

Chapter IV. Revealing the ne ural correlates of use r eff ici ent motor imagery tasks
56

Fig. 4.4 Event related spectral perturbati on for the right /left imagery trigger pull (Vo. St. ) at
electrodes C3 and C4. The color bar at the right side of the graphs show t he scale of the plots
in terms of dB for the power spectral density (-4 to 4 dB). The lateral left panel shows the
baseline mean power spectrum, and the lower pane l s show the ERSP envelope (low and high
mean dB values, relative to baseline, at each time in the epoch) . The circled areas represent
significant ERD/ERS with bootstrap level 0.01, which are found also at the 0.001 significance
level. T he ERSP was computed using the Morlet wavelet transform for the 2.9 – 35 Hz range,
on the interval -2000 – 2500ms, relative to the baseline period of -2500 – 0ms. (Figure
modified from Nicolae et al., 2016b , with permission).
Considering the tempora l and spectral s y n chronization withi n EEG, the I TC in Fig.
4.5 ilustrates the timi ng s where phase -locking o ccurs or not. Some significant ITC at about
15-20Hz for the -500 – 0 ms int erva l is observed (which ma y correspond to the decrea se in
power at 0ms in Fig. 4. 4 , bottom left plot ), wh ich might indicate therefore th at the EE G
activity becomes phas e-locked in single trials (with re spect to the sti mulus). Howeve r, the
time and frequenc y point s relating to si gnificant ITC and ERSP are not necessaril y identical
Furthermore, this effect is insi gnific ant at the 0.001 boots trap significa nce thr eshold.
Considering the other conditions , similar I TC is encountered, wh ere it hardl y re aches
significa n ce and cannot be interpreted. While searc hin g for other influences, the oscillator y
activity, for example, does not significantl y influence the ERP trace, according to I TC
(bottom plot in Fig. 4.5 ).

Fig. 4.5 Inter-Trial Coherence (ITC) for the left imagery tri gger pull ( V o. St.) at electrode
C3. T he color bar at the right side of the graphs show the strength considering coherence
(red - statistically significant phase coherence ; green – no signi ficant phase coherence ). The
bottom panel shows the mean ERP trace. Bootstrapping was used to identif y significant levels
of ITC (with 0.01 significanc e threshold). (Figure modified from N icolae et al., 2016b , with
permission).
Cons idering imagery right finge r butto n pres s wit h voice sti muli in Fi g 4.6 , more
informa tion is enc ounter ed in the be ta band, accord ing to the boots trap sig nificance level of
0.01. B eta de sy nchron ization is enc ounter ed a t 10 00 -1 500 m s, fol lowed by and beta
sy nchroniza tion in the 1500- 20 00 ms interva l in the contra latera l area for th e right move ment .
For the left moveme nt, an ERS is obser ved in the alpha band, simulta neou s with ERD in the

4.3 Findings
57
beta ban d. Regard ing the ips ilatera l informa tion, stro ng perturba tions appe ar in form of ERD
at the same tim ing re lated to the imag ery movemen t.

Fig. 4.6 ERSP at the C3 and C4 electrodes for t he right index finger motor imagery button
press task (V o. St.). The ERSP (-4 to 4 dB) was computed using the STFT method on the 2.9 –
35Hz frequency range and -2000 to 2500ms timing int erval, with -2 500 ms baseline,
highlighted by 0.01 bootstrap significance level (with the circled areas significant also for a
0.001 level).
I n the other motor imag ery activitie s, similar spectra l perturba tions are revea led
(deta iled invest igati ons wit hout bo ots trapping in N icolae e t al. ( 201 4b )).
4.3.3. 3 Neu ral co mponen ts
The tem poral e voluti on of the ERP com pone nts, as ava ilab le with the li mited spa tial
reso lution , can be obse rved in Fig. 4.7 , for the right imag ery button pre ss with vo ice st imuli.
The c ompone nt's evolut ion starts w ith a silg ht a mplitu de dec rease in the con tralatera l side,
whic h bec omes more prono unce d aro und 800 ms, d uring the pre sumed imagery movemen t
and is comp lemen ted by an increa se in the ipsilate ral side, a round 1400m s.

Fig. 4.7 Temporal evolution of the ERP components (average data) considering the right
imagery button press w ith Vo. St. ; (Figure is take n and extended from Nic olae et al., 2016b ,
with permission).
In addition, to investiga t e the contribution of the I C A components to the time serie s
(ERP) and oscillator y domain (ERSP), Fig. 4. 8 . shows, for example, I CA components
activations for all trials , considering left imag er y finger button press with visual sti muli. The
ERP image plot in Fig. 4. 8 (left side) shows an increase in amplitude (cod ed in red) observed
at 400 ms after the stimulus related to the P300 potential, and is followed by a d ecrease in
amplitude star ting from 600 ms unti l 1000ms (coded in blue), and conti nues with a sli ght
increase in amplitude for the 1000 -2000ms inter val. I n terms of varia bility between tria ls ,
differe nces e xist in the rang e 800-2000 ms, due to d iffere nt motor image ry latencie s, which

Chapter IV. Revealing the ne ural correlates of use r eff ici ent motor imagery tasks
58
slig htly influenc es als o the ERP. The other ana ly zed measure s sugge st that C omp onent 1
accoun ts for a few of the EEG power at 10 Hz (aro und 400ms in the ERSP mean powe r trace )
and for little of the avera ge ERP (ar ound 400m s and 1400m s in the avera ged ERP trace). The
phase a t the a naly z ed fre quency (9 – 11 Hz) is eve nl y distribu ted, e xcept in the 400ms ve cinity
where I TC shows a valu e of 0.7 sugge sting pha se s y nchroni zation accor ding to the boot strap
sign ificanc e level (0.0 1 ). Overa ll, the analy sis re lates to an ERP co mponent, wi th more
contri buti ons to the time serie s.
On the other hand, the secon d compone nt contr ibute s mul tiple ti mes thoroug ht the
oscil lations (ER SP at 9-11 H z) and slig htly to the ti me ser ies (to an a mount of 0.5 μV ).
Furthe rmore , the compo nent may likewi se contri bute to other oscillatory phenomena pres ent
at different frequencies, which are not visible here due t o one frequency phase-sorting. N o
sign ificant phase s y nchronizat ion is sh own gi ven by no I TC value s highe r than 0.5. The se
contri buti ons along with the pat tern spa tial distrib ution and the power spec trum of the
compone nt strongly sugge st a mu co mponen t, offering addi tiona l i nformat ion from the neu ral
oscil lations ra ther t hen th e time doma in.

Fig. 4.8 ICA compone nt s contributions to ERP and ERSP, on the time interval -500ms to
2000ms (stimuls onset at 0ms ), considering the le ft imagery button press t ask (Vis. St. ). The
small top figure shows component’s power spect rum for the 2-35Hz ban d. The ERP image
plot under show s the amplitude evolution of all trials (-2.2 μV to 2.2 μV ), phase-sort ed at the
alpha frequenc y band (9-11 Hz). The three blue trace plots underneath show: • component ’ s
contribution to the average ERP time evolution (-2.42 μV to 2.42 μV ) ; • component’s
contribution to the ERSP mean pow er at 9-11Hz (-4 to 4dB) ; • and phas e s ynchronicity ov er
all trials (ITC values in the 0 – 1 range with bootstrap significance level of 0.01 ). For an
absolute representation of component ’ s activity at an electrode (absolute value and polarity),
the components wer e bac k-projec t ed to the corres ponding channel: Component 1

Channel
1 ( C3 ) and Component 2

Channel 2 (C4) . (Figure extended from Nicolae et al., 2016b ,
with permission.)
4.3.4 C lassification of spe cific motor ima gery task s
For an overview over the di stribut ion of the data, see for example the scatter plo t of the
image r y trigge r pull da ta, sh own i n Appe ndix A. 2.1 , F ig. A .2.1 .
Aiming enhanced classification performance, the optimal regularization parameters
ga mma ( γ ), and delta ( 𝛿 ) are s earched and d etected f rom the trainin g data. In addition,
dimensionality reduction is performed by removing th e redunda nt featur es. The minim um

4.3 Findings
59
number of features that sti ll produ ce a good quality of the classifie r is selected thereafter .
Depending on each case, an optimal tra deoff has to be considere d between the model size and
accuracy . For example, in case of the data from the arm lifting motor imager y task with
visual and sound stimuli in Fig. 4.9 , the smallest optimal number of fea tures that generates a
small mean classifier error rate of about 0.2, was selected as 2600. This reduces to a fif th
portion of the features, which automaticall y decreases the c omputational complexit y of the
classifier. As it can be se en in the figure, using mor e f eatures in this case does not produce a
considerable c lassification enhancement (with less than 0.2 error rat e), while choosing less
features could weaken the classifier r eliability (m ore errors). The error rate in this case is the
misclassification error r ate, computed by the average fr a ction of misc lassified data in
crossvalidation on the whole dataset. The regularization pa rameters sele cted here for 2600
features are: γ = 0.9167 and 𝛿 = 0.1076. For other tasks, even less features are enough, e.g.
2000 features for the imag er y trigger pull task with Vo. St. (Tab. 4.3 ).

Fig. 4.9 The performance of the classifier given by the error rate, in relation to the number of
features. The data r efers to the arm li fting motor imagery task (Vis.-So. St. ) . Figure modified
from Nicolae et al., 2016b , with permission (more features).
Tab. 4.3 Features type, original features quantity and reduced feature s qua ntity

Motor imagery
tasks

Features type

Total
quantity
(number of
features)

Final
quantity
(reduce d
number of
features)

Temporal
(amplitude in
time: 300ms -
2500ms, for 0-
50Hz)

Spectral
(powe r in
time: 8-
12Hz and
18-25Hz)

Spatial
(C3 and C4
electrodes)

finger button
press ( Vis. St. )

2000

4000
(2 000 × 2)

× 2

12000
(6 000 × 2)

2000

finger button
press
( Vis.-So. St. )

2600

arm lifting
( Vis.-So. St. )

2600

finger button
press ( Vo. St. )

3000

trigger pull
( Vo. St. )

2000

Chapter IV. Revealing the ne ural correlates of use r eff ici ent motor imager y tasks
60
After setting the optima l parame ters and the number of fea tures , the mul ti-cla ss
classification is perform ed for the motor imag ery tasks (Fig. 4.10 , left image). Referr ing to
the ty pe of stim uli, h ighe st mea n perfor mance s are observed considering the visual and visual-
sound stimuli wi th 0.7 to 0.84 normalized accuracy . Good performances of 0.61-0.65 are also
observed considering the voice stimul i regarding the imager y tri gger pull a nd button press
tasks, but lower than the visual and visual-sound experiments.
Comparing between im agery tasks, the arm lifting, and finger button press with Vis.-
So. St. ex periments see m to re sult in the be st p erforma n ces (0.8 1 – 0.84), followed by the
other finger button press tasks (0.61-0.7) and the proposed efficient task of trigger pull (0.65).
Although the propo sed tr igg er pull tas k class ificat ion did not exceed the cla ssical f inger but ton
pres s tasks, the trig ger p ull tas k accurac ies are still highly significa nt ab ove cha nce le vel (t-tes t
with α = 0.01). Not ice that a ll perf ormanc es are hig h above chance level (33%), confi rmed b y
the two- sample stati stica l t-tes t at the 0.0 1% sig nifi cance level or highe r.
Furthermore, the binary cl assification results between left and righ imager y
movements, performed with the same features and settings, are si gnificantl y high o f 78 -90 %
(Fig. 4.10 , right image ).

Fig. 4.10 Average perfor mance (5 folds ) for m ulti-class (left image) and binary classification
(right image) for the mot or imagery tasks: button press (Vis. St.), button press (Vis.-So. St.),
arm lifting (Vis.-So. St.), button press (Vo. St. ) , trigger pull (Vo. St. ). The blue error bars
represent the standard error of the mean, SEM. The dotted black with gr ey horizontal line
represents the chanc e level of 33% or 50%, respe ctively. Performances are statistical ly
significant over chance leve l w ith t-test ( ‘**’ for α = 0.01; ‘***’ for α = 0.001; multi-class:
p=0.0052/ 0.0001/ 0.0004/ 0.0035/ 0.00 1; binary: 0.0002 / 0.0001/ 0.0007/ 0.0042 / 0.0093 ).
Figure modifi ed from Nicolae et al., 2016b , wit h permission (related to more classification
features) .
Moreo ver, the resu lts of the class ificat ion discri mina tion are deta iled in the confus ion
matrice s sh owing the res ubstit ution errors for eac h c lass (Fig . 4.11 ). Th e confus ion matric es
show few misclassifications between l eft and right movement, and almost no assig nments of
a movement (left or right) to the no-movement class. For example, in the trigger pull with Vo.
St . experiment (Fig. 4.11 ), 16 no-movement trials are classified as left/ri ght movements (13
left and 3 right) , 6 left movements are wrongl y classified ( 2 no-movement and 4 right), and 7
right movement trials are wrongl y classified as 2 no-movement and 5 left movements.In this
case, the classification accuracies within each class are: 0.6 for no movement , 0.7 for left
movement and 0.65 for the right movement task.

4.4 Discussion and conclusions
61

Fig. 4.11 Normalized confusion matri ces over entire dataset for the multi-class motor
imagery classification conside ring the no, left a nd right movements. The color bar on the
right relate to ratio of wrongly or correctly assigned trials (the original number of trials
within each class: 40:20:20 for no/left/right mo vement) The rounded fr action of correctly
classified samples is prese nted on the diagonal a nd fraction of o f missclassified samples in
rest. The normalized mean accuracy is shown on the bottom right corner. (Figure modified
according ly from Nicolae e t al., 2016b , with permission).
The performance of the classifier is additionall y te sted by performing the cl assification
considering shuf fled lab els and usin g th e same settings for each data, meaning the same
number of predictions and regularization parameters. In case of high performances
(significant over chance level) are still obtaine d, the reliability of the class ifier will be
cancelled, meanin g that it does not correctly disc riminate the trials, rel y ing on data a rtifacts
or other types of variabilit y in the data such as higher alpha values in case of participant
tiredness. Performing th ese shuffled labels classifi cation for each data, no result turned
positive in confirmin g that th e cl assifier does relies on a rtifacts. For ex ample, in case of the
imagery fin ger button pr ess data with visual sti muli, a mean accurac y o f 2 8% resulted, which
is significantl y at the ch ance level of 33% (p = 0. 25, Wilcox on signed rank test with alpha =
0.01).
In addition, EMG peak detection has been performed to certify no invol vement of the
muscle activity and no muscle contractions have been det ected and the sig nals show no
information in relation to the movement tasks (de t ails in Appendix A.2.2 ).
4.4 Discussion and conclusions
When performing a mot or ima g er y task, an indi vidual can be ea sil y dist racted b y ex ternal
perturbations or b y inte rnal thoug hts which m akes his’ or her attention an d focus to decrease
over time. I n order to overcome this effect, efficient user motor imagery t asks relating to
user’s background and hobb y h ave be en proposed in this study. The prim ary aim of this case
study was to preliminarily investigate effective user-defined motor imagery tasks be fore
performing an e x tended B C I study. This investigation is performe d b y anal y zing the EEG

Chapter IV. Revealing the ne ural correlates of use r eff ici ent motor imagery tasks
62
signals in terms of temporal (ERP s), spe ctral (power sp ectrum, sp ectrogram, ERS P) and
spatial information (spatial distribution over cha n nels in form of sca lp map s).
4.4.1 C omparison with p revious studie s
An accurate compa rison betwee n different studi es is difficult to be p erformed due to dif ferent
experimental settings a nd different si gnal processing scenarios and machine learning
techniques involved, bu t for th e purpose of hi ghlighting the p resented research in the
scientific communit y , so me important differentiations with the state-of-the-art studies will be
pointed out in the following.
Considering classificati on performances, sim ilar or even smaller classification
performa n ces were found in other studies, as compared to the ca se stud y in question. For
example, in Furman et al. ( 2016 ), researchers investigated imager y finger flexion as reaction
to auditory stimuli. They investigated the thumb and pink y finger flex ions of each hand, and
the simult aneous flexion of both thumbs and both pinkies, giving 6 classes in total and 30
trials for eac h task. As features, normalized covariance matric es in time and space fr om 64
channels are considered, along with normaliz ed wavelet filter bank features. The multi -class
discrimination uses a o ne versus one multi -class S VM (Support Vector Machine) with a
linear kernel classifier evaluated with 10 folds cross-validation, resulting in a single-
participant classification accuracy o f a maximum 36.2% and a minimum of 22.83%,
compared to the chance level for 6 classes of 16.71%. If we extrapolate to a 3-class
discrimination with 33.33% chance level, the result s could impl y a range o f 45.54% - 72.21%
in accuracy, smaller than the classifica tion accuracy of 61-84 % obtained in our case study for
the imagery finger flexion.
In an online BCI experiment for controllin g a virtual avatar b y i magery arm
extension, Badia et al. ( 2013 ) obtained good performances for a s imilar three class
discrimination between no imager y mov ements, left and right image ry , using a line ar
classifier regardin g alpha, beta and ga mma frequenc y bands. The classifica tion acc uracies are
similar as in our stud y , ranging from 65% to 97% with an average of 85% as compared to
81% in our arm lifting task. Whil e considering participant interest, th e participants in the
Badia et al. ( 2013 ) ex periment, ex pressed that control of the avatar’s arms was difficult,
rating on average with a 2.52 scor e out of 5. Considering combined features for th e
classification could inc rease the s y stem p erformance as in ou r stud y , and therefore ease the
control process, while contributing to an inc reased interest for the user considering more
effective a nd attractive tasks.
A recent stud y b y Rimbert et al. ( 2017 ), inv estigated and proposed the use of discrete
motor imagery (DMI) as compared to continuous motor image r y (CMI), in order to overcome
participants ’s fati gue and boredom effects in a motor imagery based BC I and to improve
efficie n c y such as d etecting f aster a motor ima gery t ask. Where C MI refers to a continuous
and repetitive execution of a motor imager y task, the DM I considers sho rt lasting ima gery
movements. As comparison, they investi ga ted 4s of four re petitive imager y right finger
flexions for the C MI an d short imager y flexions of 2s. In this wa y , the DM I movem ent
distinguished from the C M I movement b y th e repetition of the imagined movement and the
timing requested for performing the movement. Alt hough, their fin dings considering
classification show no di fference between CMI vs . rest (0.714% ) and DMI vs. rest (0.719%),

4.4 Discussion and conclusions
63
DMI c ould have a future impac t in the BCI domain regarding a faster detection of the
movement (faster inform ation transform rate), wh ile also avoid ing user’s f atigue b y reducin g
the repetitive movement and shortening the execution. Considering neuroph y siolo gy , the
grand avera g e po wer s pectrum of the DM I s howed a de creased ampli tude of the ERS
compared to the C MI t ask, which might be due to the summ ation of the ov erlapping patterns
in the CMI, more pre cisely b y the concatenation of several ERDs a nd E RSs produce d b y
severa l motor ima gery executions . In addition, a uthors detected a bigger variability between
participants for the ERD and ERS modul ations in the CM I task, which might be due to the
same reason. This variabilit y ma y also give a nega tive impact on the classification rate. In our
study, we also performed a short DM I of 3s, and in addition we us e effective tasks to improv e
participant ’s interest, the classification perfo rmance of 78-90 % for the t wo -class
discrimination is higher compared to the range o f 57 -90% in the binary classification of the
Rimbert et al. ( 2017 ) st udy. How ever, thei r experiment conside rs 16 p articipants and thei r
classification involves CSP feature s based on the spatial information from 9 electrodes,
which brings additional improvement and spatial representation and is of interest to be
investigated in the future.
Other works that suc cessfully considers feature combinations in multi -class
paradigms of imagined movements (left hand, right h and and foot) ar e p resented in Dornhege
et al., ( 2004a ) and Fazli et al., ( 2015 ) and shows once more that these combined approaches
significa ntl y boost BCI performa n ces.
4.3.5 Open que stions and conclusions
Prior to EEG analy sis the signals were preprocessed in order to remove the
interference s and noise sources and to obtain a cleaner si gna l. Th e wavelet filterin g
performe d better than t he temporal filtering, re sulting in a more increased PS NR of the
signal. N ext, the relevant components and features that relate to the corresponding motor
imagery ne ur al activity were d etected and extracted from the data.
Considering the si gnal processing steps (Section 4.3.2 ), the filterin g and artifact
removal techniques usin g w avelet filtering provided good signal quality and the non-neural
artifac ts w ere removed as possible. Next, the I ndependent Component Analysis h elped to
detect the c o rresponding motor imagery c ompone nts.
The motor im agery tasks elicited the well -known sensori-motor rh y thm s observed in
the ERD/ERS effects of the corresponding br ain hemisphere. In addition, the effect of higher
amplitude modul ations for left imag er y compared to right imagery, shown in the amplitude
evolution ove r time of the pass band data (Fig 4.3 ), is consistent with the scientific
demonstrated neural effect, which states that u ncommon activities give a higher potential
than regular activities an d stronger des ync hroniza tion in the mu-frequenc y band, i.e. for left
arm movement for right- handed user (Klöppel et al., 2007 ), or for ex traordinary compared to
ordinary actions observa t ion (Stapel et al., 2010 ).
Base d on the preli minary investigation in thi s case stud y , the ef ficient im agery tasks
relating to user’s hobby, namely the tri gger pull task, resulted in good p erformances o f the
classifier. How ever, the 'trigg er pull ' classification task resulted in a de crease as comp are d to
the basic tasks of ima gery finger butt on press and arm lifting. W hen referring to the areas and

Chapter IV. Revealing the ne ural correlates of use r eff ici ent motor imagery tasks
64
brain pro cesses involved in the mot or imager y t asks, it w as ex pected to obtain hi gher
classification a ccuracy for the arm lifting and trigger pu lling tasks as compared to fin ger
button press, because the motor imagery of a bi gger bod y p art (e. g . a rm) covers a larger area
of brain activation as co mpared to a small size body pa rt (e.g. finger) ( K andel et al., 2000 ).
Howev er , no discrepancies are obtained between arm lifting and finger button press for visual
and sound stimuli (p=0.587 two-sample t-test , α =0.0001) and similar ly for the trigger pull
task as compared to the button press task with voice sti muli (p= 0.3739 two-sample t-test,
α=0.0001) . For the tri gger pull task, because it comprises a more comple x imagery process,
involving arm lifting and finger flexion, ma ybe it becomes ha rder to be discriminated due to
many processes involved and limited spatial information available (two channels). Another
reason of this opposite performance obtain ed might be due to specific brain reaction on the
type of stimuli: voice stimuli, because smaller accuracies were obtained a lso for the finger
button press in scenario with the voice stimuli. Even so, the use of this t y pe of stimuli for
further applications is supported b y incr ease d u ser int ere st describe d in the experiment’s
questionnaire, whe re the participant stated an in crea s ed interest, performance and attention
for the voi ce stimuli . The reduced classification performances can be also due to an increased
tiredness for the participa nt at the end of th e experiment ( Vo. St. ) as compared to more energ y
in the beginning of th e ex periment ( Vis. St. , Vis.-So.St. ). All-inclusive, the hy pothesis
presented here hav e to be further investigated in a more detailed stud y , with aspec ts
highlig ht ed in the next section (Section 4.5 ).
In another tr ain of thoughts, w hen one mi ght expect not so high cl assification
performa n ces o f 61-84 % in case of imager y movements (three classes), note that these r esults
might be due to the reduced data and one participant classifica tion. The sma ll number of trials
was enforced to test the fe asibilit y of usin g a small number of repetitions t hat can be later
used in a BC I s y stem, while obtaining high class ification accuracies. The fea sibili ty of few
repetitions was suc cessful here in the offline case, although a comparison with multiple trials
repetitions is necessary i n the online case. In add ition, the combined feature approach which
considers the c ompleme ntary temporal and spectral information contributed to e nhanced
performa n ces.
On the b asis of the available neu rophysiological effects observed in the ERP s,
ERD/ERS and power spectrum, the investigated efficient motor imager y s cenarios are
feasible and can be further investi g ated in a l arger experimental study . The use of the
effective tasks in the contex t of BCI applications is reinforced b y the classification r esults ,
offering comparable performances with respec t to a basic task, while also taking into
consideration user’s interest and reducing therefore the bothersome effect within a BCI.
4.5 Limitations and future developments
As the effec tive tasks see m worthwhile to be considered for the purpose of BCI applic ations,
some limi tations and potentials flows which a re described below can be improved and
investigated further. T he investigation can be deepened in a more detailed stud y considering
more participants, e. g. 1 5 or more aiming a statis tically significant group and more channels,
e.g . 32 cha nnels or more (Sannelli et al., 2010 ). C ompared to the current case stud y in which
we could not draw concrete con clusions considering the sources and th e spatial distributi on

4.5 Limitations and future deve lopments
65
of the activity based only on two channel data, the future in-depth stud y should consider more
channels. This will prov ide a better spatial representation of the activit y over the sensori-
motor area and an efficient artifact removal. For the artifact removal case, additional chann els
in the occipital and in th e frontal area will help removing th e noise activit y generated b y th e
visual cortex and the eye movements.
In terms o f h ardware equipment and recordin g , it was inconvenient to connect
multiple channels due to many amplifier modules and lots of cables (one amplifier modul e
per channel). Furthermore, the use of active elect rodes (with buil t-in amplifier) will grea tl y
improve the EEG si gnal qualit y (M ettingVanRijn et al., 1996 ) and controll ing the impedance
will help for a better co nductivity, producing an enhanced si gnal qualit y (Ka ppenman and
Luc k, 2010 ). For future studies, it is recommended that the notch hardware filter , or other
hardware filters in general, are to be avoided whi le they tend to introduce dist orsions in the
data (Luck, 2005 ; Dick ter and Kieffaber, 2013 ), in form of phase shifts (and dela y s),
"ringing" in the data stream.
Considering experimental design, more trials can be int egra ted aimin g statistical ly
significa n ce in case of higher trial to trial variabil ity, and also as a comparison medium with
the reduced trials case. Whil e the curr ent study did not evaluate the contribution of the
amount of trials fo r BCI s y stems, a follow up stud y might be useful in thi s direction. Related
to stimuli, the enhanced visual and audio stimuli as selected in Nicolae, ( 2013 ) can b e further
investigated within the mot or im ag er y experiments (e.g . g r een stimuli on white background) .
For the sti mulus paradigm, the posi tion of the arrows in the visual sti muli experiment should
be placed in the center of the screen. This will reduce the horiz onta l eye movements and the
possible involved issue in the current stud y can be avoided, which r elates to the fact th at the
classifier ma y le arn the corresponding left or right ey e movements instead of the actual motor
imagery a ctivity. If equivalent experimental setup is desired, th en t he e y e movements
contribution ma y also b e overcome to an extent within signal processing (Section 3.2.2 ) .
Furthermore, the dissimilarities in the stimulation between the rest pe riod (no visual/audio
stimulus) and the tri al period (visual/audio stimulus) ge nerates stron g inconsistencies in the
EEG which mi ght affect the classification. Wit hout a good sp atial representation and removal
of the non-task related components, the current c lassifier might stron gly relate to the visual
brain responses generated by the external stimuli , and not to the motor imagey itself. A
simple solution requests similar stimul ation in the ex perimental paradigm for all conditions
(left, right and rest) to avoid any discrepancy.
In tems of experimental scenario, more ex periment combinations for each t y pe o f
stimuli and imagery task should be performed for more accurate comparisons: e.g. the trigger
pull task in scena rio with visual stimuli and visual with sound stimuli , in addition to the voice
stimuli experiments. Moreover, all task scenarios should be considered with e y es opened, a s
for a viabile comparison between them. On thi s line, 'eyes closed' have a differe nt amplitude
modulation, topograph y and power level as com pare d to 'e y es open ' when the alpha band is
suppressed (Berger, 1933 ; Adrian and Matthews, 1934 ; Barry et al., 2007 ; Gomez-Ramirez et
al., 2017 ). Th ese important differences produced in the EEG signals and in the neural
components might be involved in the classification differences between the tasks with visual
or sound and voice stimuli.

Chapter IV. Revealing the ne ural correlates of use r eff ici ent motor imagery tasks
66
While in the current study, the classification performances are reduced for the
proposed effective tasks as compared to the classical motor imager y tasks , a bigger range of
effective motor imagery tasks that sti mulate user’s interest and take s into consideration his
preferences is intuitively to be considered. Moreover, the tasks should be s elected to consid e r
uncommon activities in the users’ area of interest, which ma y elicit a m ore powerful brain
response, as compared to known, common ac tivit ies (Klöppel et al., 2007 ; Stapel, 2010 ).
Another aspect that has t o be in cluded is a mor e specific us er neuro -feedback in the
motor imager y trainin g process, for a b etter overview and control of use r’s own si gna ls. As
compared to the current simple fe edback, where only the evolution of the EEG sign als was
shown, a better wa y for e xample, is to consider the envelope o f the signals in the mu band for
a better representation o f the ERD and ERS effects ( Clochon, 1996 ), o r the power band
values in the mu, a lpha and beta frequenc y bands re presentative for the motor imagery
activity, or moreover, to pographical m aps of the cortical activity in the mu band (Hwang,
2009 ). Motor imager y is a complex process and requires intense concentration and attention
and as it h as be en va riousl y shown in different st udies that not all participants are capable o f
controlling their senso ri-motor rhythms (Guger et al., 2003 ; Kübler and Müller, 2007 ;
Vidaurre and Blankertz, 2010 ; Blanke rtz et al., 2010a ; Vidaurre et al., 2011b ). Therefore ,
closer investigation has to be performed on the participant s’ brain signals in the training
phase, in order to chec k t he existence of the c o rresponding mu power band modulations.
Considering the signal processing and machine l earning steps, there is al ways place
for improvements. First, automatic trial removal can be implemented ( as in Section 3.2.2.1 )
in contrast to the manual selection based on visual inspection that is used here. Further,
appropriate treatment of e y e mov ements and musc le artifacts should be considered, so the
artifac ts have low to no implications in the classifier decision. T his can be performe d b y
applying for example, the ICA m ethod with MAR A feature s election as p rese nted in Section
3.2.2.2.2.
For a more accurate ex traction of the neural information, decomposition methods can
be used, S SD or CSP for ex ample, as in Section 5.2.3.4 , for the case with a higher number of
channels. For a closer relation to the task-related neural activit y , the amplitude evolution of
the signal over time (envelope) which decreases or increases with respect to motor imagery,
can be considered inst ead of the FFT spectral features, while FFT ha s a lar g e noise sensitivi ty
and can not capture tr ansient features in a signal, nor time -fr equ ency information.
While mot or imagery produces changes in both temporal and spe ctal domains, an
interesting app roach from Lu and Yin ( 2015 ) w hich combines the ERP information with the
ERSP infor mation a nd produc es enhanced classification discrimination, is of interest to be
considered. On thi s line, it will be interesting to further evaluate the contrib ution of the motor
imagery activit y to each measure (ERP, ERSP, spectra), d etec tin g where, when and how
much is involved, exten ding the investi ga tion from Section 4.3.3.3 . I n addition, one can
consider, fo r ex ample, int ellige nt fe ature s election algorithms based on weighting (Su giy ama
et al., 2007 ) in o rder to detect which features a re the most representative for the
corre spondin g neural process, temporal or spectral, and to which extent.

4.6 Le ssons le arned
67
4.6 Lessons learned
 The motor imagery tasks elicited the well-known sensori-motor rhythms observed in the
ERD/ERS effects of the corr espondin g brain hemisphere.
 The specific mot or imager y tasks corres ponding to user back g round proved to be
efficie nt b y use r’s pro longe d attention during the exp eriment and it s subjective
evaluation stating increa s ed intere st and p erformance.
 The classification discriminations between left, right (and no imager y movement s)
considering all t y pes of motor imagery tasks resu lted in very good performances for both
multi- and binary cases, as compared also to other motor imager y studies.
 Using complementar y information from the temporal and spectral domains, brings
additional information to the classifier, pr odu cing therefore enhanced performance.
 Albeit a low number of data point s are available due to the reduced num ber of stimul i ,
the simple LDA classification method still performs ve ry good.
 The wavelet filtering m ethod resulted in b etter signa l qualit y compared to temporal
filtering, considering the current available da t a.
 After the feasibility was analy zed in this case study , the next in -depth experimental study
should consider a statistically si gnificant group of participants.
 More scenarios combinations in terms of stimuli and tasks should be further
implemented and investig at ed for appropria t e comparisons.
 More channels need to b e integra t ed in the next in -depth stud y for a more detailed spatial
distribution of the brain activity.
 To assure a correct e xecution of the motor im ag er y paradigm, a more spe cific
neurofeedback nee ds to b e implemented in the future study .
 Carefull tre atement and rejection of artifacts (ey e a nd muscle a ctivity) has to be
performe d in the future study.
 Carefull consideration is further r equested for the experimental design, to avoid
additional e y e mov ements and dissimilarities in the stimuli between conditions
(left/rig ht/r est), which might induce different visual responses.

Chapter IV. Revealing the ne ural correlates of use r eff ici ent motor imagery tasks
68

69

Chapter 5

Investig at ing the neural correlates of
cogniti ve processing levels

The functionality of ge n eral BC I s ystems are based on choosing one type of task and request
the user to perform the c orresponding mental task and voluntary control the variations of his
brain activity, such as motor-imagery based s y stems. This comes with a high drawback that
affects the user ability to control the BCI, based on the variabilit y of it s b rain si gna ls for the
same task in different moments of the d a y , or dif fere nt us er emotional sta te, and so on. Th e
idea investigated in this research stud y , is to crea t e a method to sil ently detect the user mental
st ate and effectuate the control in every moment, without requesting a strict mental task
involving extensive learning to the user side in order to control his brain signals. This will
provide a relaxed, mor e natural and faster inter action with the BCI. As an example, we can
think about an in formation seeking application, such as a research engine where the content
can b e automatically hi ghlighted, reduced, or c hanged b ased on the cu rrent ment al state
detected. In this case, the BCI will still require a training phase to infer the character istics and
thresholds for each mental state, but without the exhausting requirements for the participant
as having to continuousl y control and modif y his brain signals. This ultimate goa l is
discussed also in the future perspectives and it is validated base d on the feasibility of the
inferre d mental state d etection based on the ex perimental stud y d escribed furth er in this
chapter. Moreover, this chapter encloses the inv estigation of the cognitive user mental state
by de t ecting the corresponding ne u ral correlate s o f the depth of c o gnitive processing.
5.1 Introduction and state of the art
While Brain-Computer I nterface (BCI) res earch mostly fo cuses on the detection of
voluntary mot or controls which require intensive user trainin g and strict ta sks, as also shown
in the previous chapter (Chapter 4 ), the dete ction of the momentar y user state (Blankertz, et

Chapter V. Investiga tin g the neural corre l ates of cog nitive processing level s
70
al., 2016 ) did not re c ei ve too much attention. In r eal world applications, wh ere the int eraction
is co-adapted with the computed, the goal is to re place or supplement the explicit information
given b y the user (keyboard, mouse or gestural input) with im plicit input direc tl y from the
human brain considering the current intentions or brain state. I n such a way, the interface
should be aware of whi ch information is more significant for the use r (Acqualagna and
Blankertz, 2015 ) and access implicit information from the user state (Nicolae et al.,
2015a , b , c , 2016a , 2017a ; Ušćumlić and Blankertz , 2016 ; Wenzel et al., 2016 ) in o rder to
allow a smooth adaptation of the int erf ace according to the curren t situation . More
specifica ll y, in information seeking applications for example, wh en on e wants to search for
more information about a specific topic from a scientific onli ne database, the interface could
display meaningful ke ywords in appropria te positions in association wi th the de sired topic
and additionall y refine the results bas ed on continuous user state detection . As another
example, considering op erator monitoring applications (e.g. i n industrial w orkplace s Venthur
et al., 2010 ), the interface can reduce the number of actions that are required to be performed
by the operator and even take over control with an automated process in ex treme cases. This
approac h ma y of fer a dramatical improvement in the application b y dim inishing the number
of errors and accidents in the workplace, while avoiding critical mental states of the op era tors
and user frustration, leading to a stressless and safetier workin g environm ent.
Different mental states have been scrutinized to monitor cognitive user’ s state and
refer to diff erent levels of attention (Vecchiato et al., 2016 ), task enga ge m ent (Venthur et al.,
2010 ), fati g ue, workload (Berka et al., 2007 ; Kohlm org en et al., 2007 ; Schmidt et al., 2007 ,
2009 ; Borghini et al., 20 14 ; Schultze-Kraft et al., 2016b ), movement intention (Haufe et al.,
2011 , 2014b ) and many others (Müller et al., 2008 ; Zander and Kothe, 2011 ; van Erp et al.,
2012 ; Sturm et al., 2015 ; B lankertz et al., 2010c , 2 016 ).
Targeting information seeking a pplications, use r’s state might be also est imated b y
evaluating the current le vel of cognitive processing the presented informa tion. I n this sense,
we are intere sted in the brain natural fluctuat ions of cog nitive pro cessing (Pol ich and Kok,
1995 ), which are caused, for example, b y : mind w andering (Melinscak et al ., 2014 ; Hohmann
et al., 2016 ), dist raction (Schubert et al., 2008 ) and fluctuations in vigilance (Beatt y et al.,
1974 ; Matousek and P et ersén, 1983 , Birbaumer, 2006 ; Schmidt et al., 2007 , 2009 ; Ji et al.,
2012 ; Vecchiato et al., 2016 ). However, based on our previous ex perienc e in earlier studies
(Venthur e t al., 2010 ), these va riations are difficult to be analy zed an d validated in an
experimental setting. Therefore, the present stud y t akes the approach of inducing different
levels of cognitive processing b y task instructions. This research work aims to investigate the
feasibility of employing the depth of cognitive processing for future user state adaptation, and
to detect the feature m arke rs characteristic to different levels of c o gnitive processin g ,
exploitable in the future BC I interaction. Specificall y , the depth of cognitive processing refers
to the degree to which information can be process ed. In our scenario, the amount of cognition
spreads on three cognitive levels: from no processing, towards shallow and deep processing
(McLeod, 2007 ). W here no processing su ggests no retention of information, a shallow
process refers to a a mild proce ssing of information detected b y attention considering the
structural form, such as color appeara n ce or cat eg o rization , while a deep process requires
more elaborated p rocess es , e. g . sem antic c or relations (Craik a nd Tulvin g, 1975 ; Anderson
and Reder, 1979 ) or qua ntitative measures. While the concept of depth of cognition has been

5.2 Methods
71
widely investi ga ted in psy cholo gy (Cr aik and L ockhart, 1972 ; Cohen and W aters, 1985 ), to
the best of our knowle dge , no research has been perf o rmed that investig ates its effects
considering the electroph y siolo gica l signals towards BCI applications. Se paratel y , the EEG
activity durin g different cognitive processes (Secti on 2.4.2.1 ) ha s be en widel y investi ga ted
(Klimesch, 1996 , 1999 ; Başar et al., 1999 , 20 01 ; Debener e t al., 200 6 ). In our current
research work, three cognitive processes have been investigated, n amely memory encoding
and decoding, langua g e and vis ual imagination that can be present durin g a human-computer
interaction. The memory process requires visual or auditory memor y recall for the necessary
information (short-term memory retention of info rmation) in an n -back task form (Pesonen et
al., 2007 , Chen, et al., 2008 ), considered mostl y in the frontal, temporal and pariet al lobes
(Berger et al., 2014 ; Onton et al., 2005 ; Scholz et al., 2017 ). The language process considers
word-retrieval functions and phonemic representations, namely syllables, that are pro cessed
in the language phonology a rea (Brodmann Areas – Kandel et al., 2000 ; Lloyd, 2007 ; Dubin,
2017 ), in the pre-frontal, frontal and tempor al areas (Baars and Gage , 2010 ), mostl y
lateralized ( Bear et al., 2 007 ; Gri ggs, 2012 ). The visual imagination proc ess demands more
extensive processes like long- te rm memory retrieval and imag ination, g enerated in the pre-
frontal, central, parietal, and parietal-occipital areas (Osaka, 1984 ; Roland and Gul y ás, 1995 ;
Ganis et al., 2004, 2013 ) and uses quantitative measurements for discrimination.
The depth of cognition has been quant if ied b y t apping the corresponding components
of brain a ctivity, in a specific scenario inspi red b y the odd-ball paradi gm ( Section 5.2.1 ). The
neural components arisi ng from cognitive processes are distinguished by the Event-Related
Potentials and the oscill atory activit y. Mor eover, d iscriminative neuroph ysiologica l ma rkers
considering each level of processing are ex tracted using separability measures applied to the
ERPs waveforms and tackled b y th e spectral modul ations which are further quantified b y
enhanced classification methods based on this multivariate data a n al y sis (Section 5.2 ).
5.2 Methods
5.2.1 Exper imental design to el icit different c ognitive pro cessing leve ls
5.2.1.1 Participants
The ex periment was conducted in a laboratory en vironment with s eventee n participants aged
between 22 and 35 y e ars old. The experimental study involving human pa rticipants described
in thi s research work was approved b y the ethics c ommittee of the Department of Ps ychology
and Er gonomics of the Technische Universität Berlin. Normal or correct- to -normal visual
acuity was c onsidered for the study and no participant e x pressed a histo ry of neurological
disease, injur y or h eart problems . The data from t wo participants was discarded from further
analy sis du e to improper recording. Considering the ex perienc e in BCI from the remaining
fifteen participants, the group was mix ed, rang in g from no ex perie nce in BCI ( 5 p articipants )
to familiar (4 participants) and ex perts in B CI (6 participants). Participants had different
mother tongue s, namel y German (11 partic ip ants), English (one pa rticipant) and oth er
languages (3 participants), therefore a good com mand of English or Ge rman was required in
order to fulfill the task in the lang uage condition. Twelve participants we re ri g ht handed and

Chapter V. Investiga tin g the neural correlates of cognitive pr oc essing l evels
72
with regard to gender, 4 females and 11 males t ook part in the experiment. To countenance
their motivation, participants were financially remunerated for their participation .
5.2.1.2 E xperimental scenar io
The ex periment started with an introductor y dis cussion for the participant into the
experiment, followed by practice tests in order to familiarize with th e tasks. The total
duration of the experiment lasted between three and three and half hours , depend in g on the
intr oductory discussion time and the time requested by each participant to g et familiarized
with the experiment.
The experiment elicits the levels of cognitive proc essing b y c onsid ering a visual
experimental paradigm similar to an odd-ball paradig m. The use r is requested to stay still,
relaxed and seated with 30 cm in front of the LCD screen and focus in the center of the
scree n while visuali zing the sequence of sti muli and performing the mental tasks as
instructed.
1. Stimuli
The visual stimuli are repre sented b y a pair of cartoon -drawing ima ge s ha ving same
color (red/ g reen/blue/magenta) and showing a different object chosen fro m one out of three
categorie s (animals, fruits and mobi lity) (example in Fig. 5.1 ). The pair of images is formed
by a r andom selection from a corresponding category, where each category consists of a tot al
of ten elements.

Fig 5.1 Representation of the stimuli categories (animals, fruits, mobility) and examples of
their elements ( cat, penguin, apple, banana, bus, rocket, etc.). Each ca tegory contains 10
elements and the y can b e represented in 4 color s (r ed/green/blue/magenta). For the sti muli
presentation, a pair of eleme nts are randomly selected from a category, with same color.
(Figure taken from Nicolae et al., 2017a , with permission.)
The cognitive proce sses, namel y Memor y (M), Lang u age (L) and Visual Imag ination
(VI), termed further as c onditions are evaluated i n this order, one after an other, each in fiv e
runs with 120 stimuli per run (accumula tin g a total of 15 runs and 600 stimuli). I n order to
avoid confusions betw een the tasks, which were already quite demanding, the conditions
were not alternated after each run. Eac h run of t he experiment started with a short question
considering participant’s current mood, expressed by a good, ok or bad s tate. Next, the first
image of the ex periment shows the target cue: a pair of images showing the target color and
the target c ategory (Fig. 5.2 ) . When th e particip ant is ready and h as me morized the target
information, the participant presses the space bar and the sequence of images starts. During
the presentation, the participant had to be attentive and focus ed and for ea ch stimul us it had
to distinguish the color first and then the categor y in comparison with th e target cue . To ke ep
the participant en gaged during the ex periment and to obtain a me asure o f task performance,

5.2 Methods
73
the participants were ins tructed to p erform mentall y counting and additions, in a ccordance
with the presented stimuli.
The levels of processing a re modulated b y th e r equested tasks a nd ar e triggered b y
non-targets (NT) which requires no proc essing, shallow targets (ST) associated with a
'shallow le v el' of processing and deep targets (DT) triggering a deep level of proce ssin g.
Referr in g to the t arget cue, t he instructions are as follows: i) I n case of NT (non-
targets) stimuli, the colo r does not match the target cue color → the part icipant ne glec ts the
NT sti muli; ii) For ST ( shallow tar ge ts) stimuli, only the color matches with the tar get cu e
color, but the category d oes not match → the participant performs mental counting ( +1 ) ; iii)
For DT (deep targets) sti muli, the color and the categor y m atch the target cu e → the
participant is r equested to mentall y count +1 and additionally to evaluate a question task
corre spondin g to the cognitive process (M/L/VI ) (see below Mental tasks ) and in case of
positive answer (positiv e Deep Targets, DT+) → to additionally count +10, otherwise
(negative a ns wer) do nothing additional (Nega tive Deep targets, DT-).
Participants entered the f inal result at the end of e ach run and obtained feedback about
the correct numbe r. After the experiment is completed , the participants filled in a
questionnaire detailing the difficult y of the ex periment referring to the t y pe of cognitive
processes and th e categor y t y pes. Th e difficult y is acquired b y scoring from z ero to two,
repre s enting easy, medium or hard difficult y .

Fig. 5.2 Experimental protocol examples for the cognitive processes in vestigated: memory,
language and visual ima gination (from left to right ). For each stimulus, the participants had
to perform or not mental computations and decisions. Each experiment run starts with the
current mood evaluation and ends with the final number insertion and r ece iving fee dba ck
about the correct number . Note that the presentation included in additi on the fixation and
relaxation scre ens before and after eac h sti mulus accordingly, but these screens are omitted
here. (Figure taken from Nicolae et al., 2017a , with permission.)
2. Mental tasks
Memory
For th e memor y task, resembling a complex n -back task, th e users have to memorize
the target cue pair, and while the sequence of im ages is pre sented, the y have to keep in mi nd
the last target pair which refers to a DT stimulus ( color and categor y matching the target cue).

Chapter V. Investiga tin g the neural corre l ates of cog nitive processing level s
74
For each DT stimuli, the users have to evaluate if at l east one of the current stimulus elements
was also present in the l ast stimulus target pair. I n case of a positive answer (positive deep
target), the y have to me ntally add 10 to the current number counted and memorize the new
target pair for the ne x t trials in any of the cases.
Language
The language task r efers to the words that represent the image elements. The user h as
to decide wh ether the number of s yllables of the element in the left side is g reater or equal
than the number of s yllables of the ri ght element . Again, if the answer is t rue, add 10 to the
number counted. En glish or German language was considered, depending on the participant's
mother tongue .
Visual imagination
For th e visual ima gination task, use rs hav e to p erform mental representatio ns of the stimulus
elements referring to the real objects (not to the cartoon drawings) a nd make compa risons
based on dim ensions. Such as, to answer the following question: I s thr ee ti mes the dimension
of the left element greater than or equal to the ri ght element dimension? The dimensions have
to be considered as average dimensions a nd not to refer to a spe cific obj ect t y pe and ma y
refer to the entire elements size or to parts of the elements. The corresponding dimensions are
indicated with a black marker, as in Fig. 5.3 . For a constant complexit y regarding the siz e
comparisons between different trials, the selecti on of the stimul i elements pairs w hile
ge n erating the stimuli, was constraint to a threshold interval, to avoid big discrepan cies such
as too large or too small size differences.

Fig. 5.3 Example of elements size indicators: ( a) a v ertical marker p laced on left side
represents the entire object ; b) a v ertical marke r placed on the right side represents a part of
the object (i.e. elephant’s ear ); c) a horizontal marker for a front view representation
indicates element’s widt h; d) a horizontal marker for a lateral view repre sentation indicates
element ’s l ength. (Figure taken from Nicolae e t al., 2015a , wit h permission, where the
images a) and b) were d esigned by Freepik http://www.freepik.com/ )
5.2.1.3 E xperimental design
The timing, the speed and the complexit y of the stimul i presentation was set based on
a pilot study without the EEG cap on four participants. The timing of each trial is represented
in Fig. 5.4 , starting wit h 500ms for fix ation, continuing with the stimulation period of
1250ms and followed b y the relaxation period of 750ms , with a tot al of 2500ms I nter-Stimul i
Interval.
The average ratio of the stimuli was chosen to b e 75:12.5:12.5 ±2% for NT:ST:DT.
The number of de ep tar ge ts ( DT) and shallow t argets (ST) was chosen t o be approximately
the same, such as the dif fere n ce in the ERPs will not be correlated to the frequency of their
occurrence. Wit hin the deep tar ge ts, approxim ately 36 % are n egative deep targets (DT-) and
64% are positive deep targets (DT+). I n order to maintain this occurr en ce and not to increase
too much the v ariability and the complexit y of the experiment, onl y 2/ 3 categ o ries w ere

5.2 Methods
75
selected for each run. The order of the sequence was set to start with an e asier t arget category
(fewer object details, tra nsportation or fruits), to continue with a harder categor y (more object
details: animals) and to end with an easier category (transportation or fruits).

Fig. 5.4 T iming of the trial: 500ms for the fixation cross ( whit e), 1250ms for the sti mulation
period and 750ms for the relaxation period. The stimulus background screen is light grey.
(Figure taken from Nicolae et al., 2017a , with permission.)
The stimuli presentation was developed with the Processing software version 3.0a4
( https://processing.org / ). The images (cartoon-like drawings) were drawn with the I nkscape
software ( https://inkscape.org ), except from the elements in the animals categor y , which were
downloaded f rom an on -line free d atabase ( http:/ /www.fr eepik.com/ ). The ima ge s fo rmat
used is Scalable Vector Graphics (SVG) with a resolution of 480x480. The images were
placed close to the center of the screen with a 2" distance between them, presented on a 23"
scree n with 60Hz refresh rate a nd 1920x 1080 resolution (Dell U2410).
5.2.2 N eural signals acquisition
The neu ral si gna ls we re rec orded using 64 EEG ch annels using the Ac ti CAP sy stem with
active electrodes ( Brain P roducts GmbH, Munich, German y ) and distributed according to 10-
20 international s y stem. One electrode was placed under the left ey e to record the EOG signal
movements. Unipol ar recording was used with t he ground placed at the AFz scalp position
and referenced at left and right mastoids. For good qualit y recording, the skin – electrode
impedance was kept belo w 20 kΩ using the ActiCAP control software. Signals were recorde d
with BrainVision Recorder at 1 kHz sampling frequency (fs) and amplified using BrainAmp
MR plus . The main script to connect between the software products was developed in
MATLAB (release R2014a, The MathWorks, Inc., Natick, MA, USA) . The recorded EEG
and behavioral data is a vailable from the D epositOnce repository o f Technische Universität
Berlin (Nicolae et al., 2017b ).
5.2.3 A nalysis s t ra t egy t o detect the neur al c orrelates of different level s
of cognit ive processing
The signal pro cessing methods and machine learning techniques fo r data anal y sis
were d eveloped with the MATLAB so ftware (Th e MathW orks, Natick, MA, USA), and the
BB C I toolbox (Blankertz et al., 2016 ; https://github.com/bbci/bbci_public ), the MAT LAB
Signal Proce ssin g Toolbox, the Statistics and Machine L earnin g Tooolbox, th e Wavelet
Toolbox and the EEGLAB toolbox (Makeig et al., 2000a ; De lorme and Makeig, 2004 ).
Furthermore, for more complex and vivi d graphical representations the P y t hon programming
language was used a lon g with the package Matplotlib (Hunter, 2007 ).

Chapter V. Investiga tin g the neural corre l ates of cog nitive processing level s
76
5.2.3.1 B ehavioral assessment
As described in the experimental scenario sectio n, subj ective indicato rs were considered
for the participant’s behavioral assessment, referring to the current personal mood (good, ok
and bad state) and p ersonal overview for the difficult y of the conditions (0 - ea s y, 1 -
medium, 2 - hard). In addition, an objective indicator was also integrated in relation to
participant’s r esponses t hat refe rs to the number counted. This was assessed b y a ratio of the
absolute differenc e b etween the participant ’s response and the correct num ber, divided b y the
correct number (Eq. 5.1 ). The resulted ratio varies between 0 and 1, showing high or weak
performa n ce. The final assessment value was computed for each condition and av eraged
between all runs.

5
5
1
,



 r r
r r
C p
c
c pr
a

, (5.1)
where
a p , C – the assessment value for the behavioral data of the participant p ∈

15 1 

for the
condition C (M , L or VI ) ;
pr r – the participant’s response for the run r, r ∈

5 1 

;
c r – the correc t numbe r for the run r.
5.2.3.2 Pre-processing (f ilt ering and epochs reject ion )
Downsampling to 100 Hz was performed ( Section 3.2.1.2 ) in order to reduce the
dimensionality o f the da ta, while prese rvin g onl y the related human brain freque n cy range .
The data was then low-pass filtered for a nti -aliasing using the Chebyshev ty pe II filter of
order 10, with a 42 Hz pa ss -band edge frequenc y and less than 3 dB ripple and a 49 Hz stop-
band edge frequenc y with 50 dB attenuation. In addition, for removing the signal drifts, a 1
Hz FIR high-pass filteri ng of order 300 with zero-phase shift, desi gned using least-squares
error minimization, was applied (For future online classification, appropriate causal filters
must be considered). The signals were re-referen ced to the left and right mastoids channels.
Next, the data wa s divi ded into epochs corresponding to the ti ming described in Fig . 5.4 .
Prior to ERP analysis, ar tifact rejection was appli ed based on variance and max -min criterion
(Section 3.2.2.1 ) to r emove the epochs with o cular and muscl e a rtifacts and the channels
dropping to zero. Baseline co rrection (Section 3.2.1.4 ) was performed using 100 ms of pre-
stimulus period (fixation timing).
5.2.3.3 A rtifact removal
For a better artifact cleaning of the data such as strong e y e movement artifacts, muscular
artifac ts and loose electrodes Independent Component Anal y sis ( I CA) was applied with
artifac tu al c omponents selection given b y the MARA alg orithm (Section 3.2.2.2.2 ). T he
artifac tu al components were rejected b ased on a threshold of artifact pro bability of 0.20. In
addition, the artifac ts selection is further verified b y visual inspection of the independent
components considering the time series, components spectrum and scalp maps , and looking
for characteristics of muscle activity, e y e blinks, horiz ontal and vertical e y e movements or
loose electrodes ( e.g. in Fig. A. 3.1 ). After the a rtifactual ICs have been identified, the EEG
signal wa s reconstructed without them.

5.2 Methods
77
5.2.3.4 Mu lti-modal discriminative analysis
The multi-modal analy si s investigates the neural correlates from the temporal ( Event-
Related P otentials, ERPs) and spectral domain (Event-Related (D e)Sy n chronizations,
ERDs/ERSs), or both (Event-Related Spectral Perturbations, ERS P). As a measure of
discriminability between two classes of ea ch condit ion, the signed and squared point biserial
corre l ation coefficient ( signed r 2 , S ection 3.3.2.1.1 ) is used, showin g the difference s in the
ERPs modulations, in the power spectrum and in t he ERD/ERS effects in accordance with the
selected relevant frequency bands. Th e ERD/ER S is computed based on Hilbert Transform
(Appendix A.1.3.2 ) and the result is then smoothed (Bracewell, 1999 ). The Event-Re lated
Spectral Perturbation (E RSP) method is comput ed based on Short -Time Fourier anal ysis
(Appendix A.1.3.4.2 ) and pha se coherence a cross trials is investigated with the Inter-Trial
Coherence (ITC) measu re (Se ction 3.3.4.2 ). F or visualizing the neurophysiologica l effects,
grand averages representations of t he ERPs, spectrum and ERD/ERS curves are obt ained for
each condition b y av erag in g a cross all trials and participants. Th e bas eline interval us ed fo r
normalizing the graphical representations is selected as 200 ms before the stimulus onset,
referring to the fixation period. Changes in the oscillatory power generated at different
freque n cies are assessed b y extracting the spectrum from 3 to 40Hz, using Fourier tr ansform
with Kaiser window.
The brain sources corresponding to the neural oscillations of cog nitiv e activity a re
detected b y advanced d ecomposition methods ( Section 3.2.2.2 ), namely: Spatio-Spectral
Decomposition (SSD) and Comm on Spatial P attern (CSP). For neuroph y siological
investigation, the CSP patterns applied on SSD filtered signals are linearl y constructed in the
opposite sense as the y were applied, b y multipl y i ng the CSP patterns with the SSD patterns :
A SSD& CSP = A CSP × A SSD , where A CSP = ( W CSP T ) + and A SSD = ∑ W SSD ( W SSD T ∑ W SSD ) - 1 , as
described in Sec tion 3.2. 2.2.4 and 3.2.2.2.3 .
5.2.3.5 Mu lti-modal classification and validation
In ord er to join complementary in formation about the neural activit y, in formation contained
in different domains is combined in a f ruitful manner. S patio-temporal features based on
ERPs are combined with the oscillator y features, further des cribed below. The corresponding
level of cognitive processing is primaril y estimated by re g ula rized Linea r Discriminant
Analy sis (Section 3.4.1 ) and furth er tested b y other classification and re g r ession methods , in
order to obtain best performances. While a binar y discrimination is first ly investi gated, a
multi-class approach is f urther an al y zed to approach the d etection to wards a real applic ation
scenario. In the multi case, L DA and multinomial L ogistic R egre ssion (Section 3.4.3 ) are
investigated as classifiers.

Chapter V. Investiga tin g the neural correlates of cognitive processing l evels
78
 Classification features
1. Combined spatio-temporal and spatio-spectral features (for binary
classification)
a) Spatio-temporal features
The spatio-temporal features (channels and time) were extracted as described in Blankertz et
al., ( 2011 ) and Section 3.3.2.1 . Five tempor al windows are d etected for each participant
based on a heuristic selection of the interva ls with maximum signed r 2 discrimina bilit y and a
constant pattern betwe en the two classes. The windows were se arc h ed over th e stimul us
interval, from 0 to 1250ms.
b) Spatio-spectral features
As de scribed in Section 2.4.2.1 , the cog nitive processes also genera te modifications in th e
oscillatory activit y . Therefore, we considered extra ctin g the valuable information from the
involving frequenc y ba nds according to each cognitive process. Th e most significant
freque n c y bands were selected based on the discriminative anal y sis (S ection 5.2.3.4 ). To
enhance the discrimination of activity in the frequency band of interest, Spatio -Spectral
Decomposition (SS D) was performed (Nikulin et al., 2011 ) and the respective Common
Spatial Patterns (CSP s) (Blankertz et al., 2008c ) were ex tracted for the corresponding
freque n c y bands.
 Spatio-Spectral Decomposition, SSD
Aiming an enh anced discrimination of mental states expressed b y the sp ectra l modulations
depicted b y the ERD/ERS phenomena , linear spatial filtering was applied prior to CSP
detection. The neural activit y of int ere st can be c overt in the fluctuations of th e background
noise activit y due to overlapping frequency content or to reduced variance for the activit y of
interest as compared to a background process. Therefore, it seems useful to apply SSD
(Section 3.2.2.2.3 ) in order to enhanc e the variance in the frequency range specific to the t y pe
and the level of cognitive processin g, and reduce the variance of the noise. Specific all y, t he
optimal spatial filters are detec ted by m aximizing the frequencies of interest, considering the
alpha and beta frequency bands selected by the signed r 2 discriminability m easure over the
E RD /ERS modulations (as will be shown in s ection 5.2.2.2 ), and min imizing the sig nal
power in the nei ghboring frequencies. The neighboring frequencies are consider ed a s 1H z
band width aside the frequency band of int ere st , followed or respectively preceded b y a gap
(stop band) of 1 Hz. Because this re quires frequency filtering be fore the decomposition,
continuous data was used to avoid filter edg es artifacts. Following closel y the decomposition,
SSD extracts the components primaril y expressi ng th e signal of interest t hat better relates to
the oscillation’s variance. Thus, for the SSD decomposition we consider onl y th e hi ghest
components that contribute to 10 -6 of the hi ghest eigenvalue (see low -rank factorization in
Haufe, et al., 2014b ), which generally r esulted in about 15 to 35 components per
discrimination pair.

5.2 Methods
79
 Multi-band Common Spatial Patterns, mCSP
In order to reduce the eff ects of volume conduction and to decode the cognitive intentions b y
distinguishing the spatial localization of the oscillatory activit y , we make use of the widely
used Common S patial P atterns (CSP) method as described in Section 3.2.2. 2.4 , successful as
shown in different studi es ( Winkler et al., 2011 ; Acqualagna et al., 2015 ; Acqualagna et al.,
2016 ; Schultze-Kraft et al., 2016b ). In our case, the binar y technique increases the variance of
a hi g her level o f cognitive processing while di minishes the variance o f a low er level o f
processing and vice v ersa. The components reaching this goal were automatically s elected (as
in Blankertz et al., 2008 ) to a max imum of three spatial filters per class and individually
checked for each participant with c areful visual i nspection. As highlighted b y the ERD/ER S
phenomena (Se ction 5.3.2.2.2 ), the relevant time i nterva l characterizing the cognitive activit y
was selected sta rting f rom 350 ms after the sti muli and until 2000ms, approximatel y
corre spondin g to the moment when the co gnitive processing sta rts, after t he appeareance of
the P300. In addition, more specific int ervals were considered as given b y the ERD/ERS
discriminability , in accord an ce with the discrimin ation pair (e. g. 800-1500 ms for the ST-DT
discrimination) and the results are presented in S ection 5.3.2.2.2 . A deeper cognitive
processing requires longer time to be fulfilled, ti me which ex ceeds the stimul us presentation,
as described also in Duncan-Johnson and Kopell ( 1981 ). The most significant CSP s
repre s entative for the relevant frequenc y bands, alpha (8 -14 Hz) and beta (16-20 Hz), are
detected as given b y dis criminative analysis (Section 5.3.2.2.3 ). The CSPs we re separatel y
computed on e ach band , the lo garithm of the variance is computed for e ach CS P (Section
3.3.3.1 ) and all values are finall y cumulated as ba nd power features in the feature vector. Th e
entire process wa s p erformed on the pre cu rsory band-pass filtered data with SSD.
c) Combined spatio-temporal and spatio-spectral features ( ERP-mCSP )
Furthermore, the spatio -temporal and spatio-sp ectra l features were combi ned, such that
complementary informa tion from both domains will be simultaneously ex ploited. The
corre spondin g processing pip eline is d escribed in Fig. 5.5 . Firstl y , th e si gnals are pr eliminary
preprocessed (b y filtering, epochs and channels rejec tion and artifact removal) and
segmented, follow ed b y appropriate f eature extraction considering the feature t y pe: time or
power. The obtained optimal spatio-temporal fe atures related to the highest signed r 2
differe n ce between classes are concatenated with the spatio-spectra l features given b y the
mCSP process, while considering the cross-validation scheme (10 folds with 10 repetitions) .
Specifically, in the spectral domain, due to label information integra tion, the releva nt CSPs
are computed on th e training data (CSP W) and used to spatiall y filter t he testing data by
linear derivation (W), for each cross-validation fold . The resulted band-power features (lo g-
variance) are introduced in the feature vector along with the relevant temporal features. W hile
in our case, the features are roughl y on the same scale, no normalization was initiall y
performe d on the feature vectors. However, for comparison purposes, z -score normaliz ation
of the feature v ectors was later applied b y subt racting the mean and dividi ng b y the st andard
deviation on eac h feature type.

Chapter V. Investiga tin g the neural corre l ates of cog nitive processing level s
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 Classification and regression

a) LDA Classification
The discrimination was first evaluated b y regularized L inear Discrimi nant Analy sis with
shrinkage of the covarian ce matrix ( Section 3.4.1.1 ), where the optimal shr inkage parameters
are a utomatic ally d etermined (as in Blankertz et al., 2011 ; S chäfe r and Strimm er, 2005 ).
b) sliding LDA classification
Separately, a shrinkage LDA classifi er (similar a s in Ušćumlić, 2016 ) is applied onl y for the
temporal f eatures, in sl iding manner for each trial with 50ms ti me shifts on different
significa nt intervals dep ending on the condition. The intervals are selected acco rding to the
discriminability evaluati ons. Then, the classifier is set to slide from 0 to 1500ms for the
memory condition, until 1000ms for the language and until 1400ms for the visual imagination
condition. Preliminary, the classifier is trained on the 55 0-650ms interval for memor y, 850 -
950ms for language and 800 -900ms for visual imagination. The final result considers average
performa n ce for a trial.
c) QDA
In addition to L DA, Quadratic Disc riminant An aly sis (Section 3.4.1.3 ), was also applied to
investigate the p erformance. The results are further provided in Section 5.3. 3.3.1 .
d) Regression
Furthermore, regression approac h es are also investigated (Section 5.3.3.3. 1 ), namel y Ridge
Regression shrink (Sec ti on 3.4.4.1 ) and Logistic Regression (Section 3.4.2 ).
 Validation
The pairwise classification was validated with 10 folds cross-validation (Section 3.4.5.1 ) and
the performance is assessed by the area unde r the ROC curve scores (Section 3.4.6.1 ). For the
regre ssion c ase, class-wise normalized loss measure (Section 3.4.6.2 ) was applied.

81

Fig. 5.5 Data analysis diagr am. The neural data is analyzed in the temporal (ERPs), spatial and spectral domains ( α, 8 – 14 Hz and β, 16 – 20
Hz) . T he three types of feature vectors: the sp atio -temporal features (the most relevant tempo ral points based on the maximum signed r 2
intervals between classes), th e spatio -spectral fe atures (log-band power of the C SPs) in th e α and β bands, are concaten ated and given to the
classifier within crossvalidation. Due to label information employment, spat io-spectral filtering considers the optimal channel and frequency
band using CSP analysis with automatic fi lter selec tion computed on the training set (CSP W) and applied to the test set by linear derivation
(W). SSD and the interval selection method based on signed r 2 were applied to the entire dataset and not w ithin crossvalidation. W hile thi s
aspect of the validation is not perfectly sound, the expected overestimation of the performance is limited. Final ly, the c lassif ier (regularized
Linear Discriminant Analysis with shrinkage of the covariance matrix ) decides the corresponding class membe rship fo r each trial (output
classifier), re pr esenting the cognitive processing lev el. (Figure taken from Nicolae et al., 2017a , with permission.)

82
2. Combined spatio-temporal an d spatio -spect ral features (for multi -class
classification)
In relation to the combined approach d escribed in the previous sub -section for binar y
classification ( 5.2.3.5.1 ), few modifications have to be implemented. While the signed r 2
discrimination as well as the CSP method are specific to binar y discrimination, appropria te
discrimination approac h es have to b e considere d, applicable fo r multi-class classification.
The temporal fe ature s are selected consid ering the relevant and unique si gned r 2 intervals for
the ST-DT and ST-NT discrimination pairs. Reg arding spectral features, m ultiple CSPs are
computed with the J AD method (Section 3.2.2.2 .4.2 ) selecting two CSPs per class and the
classifier performances a re c ompa red when using the CSP feature s with the OVR strateg y
(Section 3.2.2.2.4.2 ). Since this algorithm makes use of the label information, the CSPs were
computed in crossvalidation form, in the same wa y as the binary CSP approac h in sec tion
5.2.3.5.1 . Similarly , the log-variance features (section 3.3.3.1 ) are conside red for
classification, for both frequency bands and preliminary SSD filtered data .
Linear Discriminant An aly sis with shrinkage of the covaria n ce matri x (Section
3.4.1.1 ) is employe d for classification. In additio n, mult inomial logistic re gression (Section
3.4.3 ) is also investigated (Section 5.3.3.3.2 ) . The classifiers are evaluated with the class-wise
normalized loss measure (Section 3.4.6.2 ). For more details regarding the accuracy for each
class, normalized confusion matrices (Section 3.4.6.3 ) were computed.

5.3 Findings
83
5.3 Findings
Firstly , the behavioral performances we re stu died, followed b y the neuroph y siological
analy sis and interpretation, which g ives detailed insights for the corresponding cognitive
processes.
5.3.1 Invest igating behavior al m easures
The behavioral data was anal y zed considering the ratio of pa rticipants responses (described in
Section 5.2.3.1 ), the pa rticipants state evaluated at the beginning of each run, and participants
feedback regarding the application in terms of interest a nd complexity. C onsidering user’s
mood, eleven pa rticipants expressed a ‘ good ’ condit ion for 53% of the ex periment and six
participants maintained t heir ‘ok’ mood fo r 47% of the experiment. No single pa rticipant
reported a bad mood during or after the experiment.
Regarding participants r esponses (Fig. 5.6 , left side), more accurate responses are
observed for the lan guage condition, with the 25% and 75% percentiles of the ratio closer to
zero (however, this effect is not statisticall y si gnificant: p = 0.2465 with 1-wa y indep endent
rm -ANOVA). Referring to a trend ove r ti me con sidering p erf o rmance, n o improvement or
decrease was obse rved c onsidering the answers r atio, showing insi gnificant correlations b y
the Spearman rank-o rder corr el ation (memory : p = 0.3367; lang uage p = 0.3982; visual
imagination p = 0.1211; and in total over all 15 runs: p = 0.6192).
Considering tasks difficult y for each condition (Fig . 5.6 , right side), lower score is
depicted in the langua ge condition, characterizing an easier pro cess rated by the participants
(statistically significant: p = 0.0451, one-way ANOVA statis tical test considering one factor
(participants) and three levels ( conditions)). The memory and visu al imagination conditions
rece iv ed equal scoring representing similar diffic u lty, higher than the language condition.

Fig. 5.6 Behavioral assesment – objectiv e and subjective indicators for the Memory (M) ,
Language (L) and Visual Imagination condition (VI): answers ratio ( left image ); difficulty
scores (right image) . R atios closer to zero correspond to better performance. The blue
asterisk indicates the corresponding mean valu es and the outliers are repr esented by the red
crosses. ( Figur e taken from Nicolae et al., 2017a , and adapted from N icolae et al., 2015b ,
with permission).

Chapter V. Investiga tin g the neural corre l ates of cog nitive processing level s
84
5.3.2 Extr acting the neur al c orrelates for ea ch cogniti ve processing le vel
5.3.2.1 Temporal analysis
The first view over the neurophysiolog ical characteristics in relation to the processing of the
cog nitive tasks considers the event- related potenti als (ERP s). The following figure (Fig. 5.7 )
shows the grand averages for each condition ac ross all pa rticipants and trials. S trong
differe n ces between the levels of processing are depicted considering amplit ude and duration
of the potentials. No strong differences ar e encountered in the temporal ev olution of the first
potential peaking at ap proxim ate 250ms , across all conditions, characterized by a fr onto-
central positive c ompon ent, representing visual processing of the external stimul i. However, a
negative component at the lateral occipital location is present in this early interval, more
pronounced in the visual im ag ination condit ion. Its amplitude is graduall y modul ated by th e
type of ta rget (DT < S T < NT). Furthermore, d ifferent amplit udes and duration are
encountered for the later posit ive component, corresponding to the P300 potential, present at
400ms. I t is characterized by a strong positive activation in the centro-parietal area and is
modulated in amplitude by an increasing lev el of cognitive processing (NT < ST < DT). For
an overview over the E RPs on all elec trodes, see Appendix A.3.2 , Fig . A. 3.6 and for an
example on single partic i pant ERPs, see Appendix A.3.2 , Fig. A. 3.4 .

Fig. 5.7 Grand average ERPs. ( a ) Memory condition; b) Language; c ) Visual imaginat ion.
The upper plots represent the time evolution from -200 ms to 1250 ms , with 0ms, the stimulus
onset, at representative electrode Cz. The scal p plots underneath show the topographies
reffering to the shad ed areas highlighted in the time evolution plots: 210 – 260 ms; 360 – 460
ms . (Figure tak en from Nicolae et al., 2017a and adapted from Nicolae et al., 2015a , with
permission.)
Two decisions are involved in each task. Firstl y , partic ipants decide on the type of the
stimulus (NT, ST or DT), which further triggers no (NT), mild (ST), or intense (DT)
processing. The related processes according to the decisions influen ce th e ERPs exposed in
Fig . 5.7 . While the co mponents amplitudes are graded in accordance with the level of
processing, their latenc ie s are similar, re presenting the same dec ision task. For deep targets
(DT), a second decision has to be performed (more detail in App endix A.3.2 , Fig. A. 3.5 ). In
this case, r epre s entative ERP components are not clearly distinguishable, due to different
time execution variations between trials (Appendix A.3.2 , Fig . A. 3.3 ).
Grand average signed r² differences between the levels of processing are anal y z ed
referring to the sp atial distribution shown as scalp topographies in the to p plots and to the
temporal discriminabilit y presented in the underneath time plots ( Fig . 5.8 ). However, because

5.3 Findings
85
the r² values depend on the number of samples, the comparison across different pairs w ith
respec t to the absolute value of r² is biased, because NTs ar e in greather nu mber than ST and
DT. The highest discrimi nation between the levels of processin g is encountered in the centro -
parietal area, corresponding to the P 300 component. The amplitude and duration of these
P300 differe nces differ b etween conditions and indi cate different cogni tive processing of the
stimuli. While diffe renti ating b etween a deeper level of p rocessing and an easier l evel (ST -
NT, DT-NT, DT-ST), th e most prominent signed r 2 difference can be des cribed as a positive
deviation starting around 300 ms and gradually increasing until 1000 ms or less, strictl y
depending on the condition.

Fig. 5.8 Grand- average discrimination s of the Event-Related Potential s given by signed r 2
considering Memory, L anguage and Visual imagination conditions for the -200 ms to 2000
ms interval. From top to bottom: ST – NT and DT – NT at channels CPz (thick line) and O1
(thin line), and DT – ST at channels CPz (thick) and C3 (thin). The scalp topogr aphies
correspond to the time i ntervals highlighted by t he shaded areas in the temporal evolution
plots: 210-260 ms ; 360 -460 ms; 800-900 ms; 1450 -1550 ms. ( Figure taken from Nicolae et
al., 2015b , with permission from Springer Publication.)
In addition to the other discrimination pairs, the de ep and shallow amplitud e evolution
discrimination (DT-ST) shows a late activation in form of a n egative dif ference around 850
ms for the lan guage condition and a positi ve difference, a round 1500 ms for the memor y and
visual imagination proce sses. Left lateraliz ed sp atial dist ribution is observed in form of a
decreased activation, specificall y in the language condition. The potential at 1500 ms (more
detail in App endix A.3.2 , Fig. A. 3.2 ) resemble a P 2 potential cor responding to the
appearance of the blan k s cree n (relaxation period) and peaking 250 ms after. These potentials
are simil ar for no - and s hallow processing, but f or deep processing the p otential is higher .

Chapter V. Investiga tin g the neural corre l ates of cog nitive processing level s
86
Except the biased signe d r 2 measure, another plausible reason might relate to the trend of the
ongoing si gnal which is higher in amplit ude for a longer period of ti me for DT (see Fig. 5.7) ,
due to the additional decision which require s lon ger time to be fulfilled.
For a detailed view of the discriminabilit y ov er a ll channels, the signed r 2 computed
pair-wise between cl asses using additional Bonferr oni co rrection is shown in Appendix
A.3.2 , Fig. A.3.7 .
5.3.2.2 Spectral analysis
1. Power spectrum
The neuroph y siolo gical markers th at represent t he cognitive pro cesses are complementary
investigated in th e power spe ctrum by evaluating the neural a ctivit y generated in theta (5 -
7Hz), alpha (8-14 Hz) and beta (16 -20Hz) fr equency bands, as depicted in Fig. 5.9 , computed
on the trial tim ing 350-2000ms. Discriminative infor mation can be observed over the alpha
(8 -12Hz and 12-14Hz), showing des y nch ronization with 2-3 dB less during de ep processing
as compared with shallow processing. The theta (5-7Hz) a nd be ta (16-20Hz) bands do not
show substantial differe n ces.

Fig. 5.9 Grand average power spectrum at location Pz, computed on the 35 0-2000ms timing,
for the Deep (DT), shall ow ( S T ) and no-processing (NT) l evels in case of memory, language
and visual imagination conditions. The top plots show the power spectrum at dif ferent
frequency bands and the bottom plots show the scalp maps corresponding to the grey shaded
areas from above. (Figure adapted – different timing, from Nicolae et al., 2016a , with
permission from Verlag der TU Graz .)
A closer investi g ation of the neura l fluctuations of the cognitive processes in the
spectral domain is reve aled b y the si gned r 2 discriminabilit y , mor e pronounced at the parietal
location, presented in Fig. 5.10 . This helps depicting the most informative frequency bands
involving the co gnitive p roce sses. Higher dis crimination is observed for the alpha band (8 -14
Hz) followed b y a small er discrepanc y in the beta band (16-20 Hz). Considering their
prominent difference encountered in the frontal and parietal sites, both frequenc y bands were
selected fo r further anal y sis in a multi -band approach. A small modulation is visi ble also in
the theta band (5-7 Hz), observed for the shallow and no-processing d iscrimination and
between deep and shallo w processing ( the upper and bott om gra phs in Fig. 5.10 ). This effect
in the theta band is insignifica nt compared to the discrimination in the alpha and beta
freque n c y bands and it i s not found for all pro cessing levels, therefore the theta b and is not
considered further for the analy sis.

5.3 Findings
87
Continuing the investi gation, more pronounced difference in the spectrum i s o bserved
for the deep -shallow discrimination, as compared to the shallow and no -processing
discrimination. This effe ct coincides with the fact that complex processing has a stron ger
influence in power compared to a superficial level of processing ( J aušovec and J aušovec ,
2000 ; She et al., 2012 ; Naumann et al., 2017 ). However, it should be n oted that the s ame
issue occurs here related to the biased r² values which depend on th e number of samples,
therefore more bi ased towards the NT case.

Fig. 5.10 Grand-av erage spectrum discriminability given by sign ed r 2 at location Pz. From
top to bott om: ST -NT, DT-NT, DT-ST discriminations, computed on the 3 50-2000ms timing
for ST-NT and DT-NT and 800-2000ms for DT -ST. T he four sca lp plots refer to
discriminative signed r 2 values, corresponding to theta (5-7Hz), alpha (8-12 Hz ; 12-14Hz),
and beta (16-20Hz ) freq uency bands (shaded in grey) . Note the scale difference: the upper
ST- NT graphs scale from -0.005 to 0.005 sgn r 2 , compared to th e other graphs which scale
from -0.035 to 0.035 sgn r 2 . (Figure adapted – different timing, from N ic olae et al., 2017a ,
with permission from Springer.)
2. ERDs/ERSs
Further , a deeper overvi ew of the modulations in different frequ ency b ands, such as
the (de)synchronization (ERD/ERS) effects, outlined b y the modulation o f the amplitudes in
the temporal domain, resp. the hull curves of the specific chosen bands is investigated in Fig.
5.11 and Fig. 5.12 . For appropriate visualiz ation, considering parietal area as the main area
for cognitive processes , the midline parietal electrode Pz is selected (For other electrode
patterns, see Fig. A. 3.9, A.3.10 in Appendix A.3.3 .). The envelope ran g es from -1 to 0.5µV
(Fig. 5.11 ) for the alph a band and between -0.4 to 0.4 µV for the beta b and (Fig. 5.12 ). The
evolution of the envelopes begins with a similar synchronization for all processing levels and
conditions until 300 ms, corresponding to the same ty pe of processing the external

Chapter V. Investiga tin g the neural corre l ates of cog nitive processing level s
88
information. Further, des y nch ronization follows in both alpha (8 -14Hz) and beta (16-20Hz)
freque n c y bands, stronger around 500 ms, showing use r’s pr eparation fo r a more complex
cog nitive t ask. Next it is completed b y a s y nchron ization starting around 8 00 ms and peaking
at 1800 ms . The amplitude e volution of the hull curves is modulated by the amount of
processing: the shallow (ST) and deep processes (DT) elicits more pronounced
desy n chronizations in compar ison to the reference of no-processing (NT). I n addition, the
deep p rocess has a more pronounced ERD compared with the shallow process (signed r 2
discriminability in Fig. A. 3.8 of Appe ndix A.3.3 ).
Considering the spatial distributions, more pronounce d synchronization (0.1 – 0.35μV)
is observed for shallow p rocessing in th e centro-p arietal area co mpared to no-proc essin g (0 –
0.25μV) and stronger desy nch ronization (- 0.3 – 0μV ) in the temporal, central and parietal area
for complex processing. When comparing b etween complex cognitive processes (DT ), higher
ERS in amplit ude and s patial distribution is found for the memor y processes, in opposition
with a stronger ERD for language and visual imagination. This effect might be explained b y
the fact that visual ima gination is a more complex process, r equiring additional processes and
functions, namel y memor y and ima g ination, contrasting with the oth er two processes . On the
other way around, the memor y p roce ss produ ces th e highe st im pact in am plitude and reduced
spatial distribution, contra sting with the behavioral point of view, whe re the p articipants
stated, on average the l anguage as the easiest method.

Fig. 5.11 Grand-average ERDs for the alpha band (8-14 Hz ) considering all conditions (M,
L, VI) and processing levels (NT, ST , DT) at electrode Pz. The baseline int erval is equivalent
to 200 ms o f pre-stimulus interval ( the grey horizontal strip). The amplitude limits for all
graphs is -1 to 1µV for the time e volution and -0.35 to 0.35µ V for the scalp dist ributions. The
four scalp plots are computed on : 210 – 260 ms, 360 – 460 ms, 800 – 1200 ms, 1200 – 1600
ms int ervals (shaded in grey) . (Figure from Ni colae et al., 2017a , w ith permission from
Springer.)

5.3 Findings
89

Fig. 5.12 Grand-average ERDs for the beta band ( 16 -20 Hz ) at electrode Pz for all conditions
(M, L, VI) and proc essing levels (NT, ST, DT) an d baseline corrected (- 200 - 0ms). Amplitude
limits for all graphs : -0.4 to 0.4 µV for the temporal ev olution and -0.22 to 0.22µ V for the
spatial distributions. (Figure from Nicolae et al., 2017a , with permission from Springer.)
3. CSPs
Furthermore, the C ommon Spatial Patters anal y s is provide information about the presumed
sources of the neural activit y which are optimall y p rojected on the surfa ce o f the scalp (as
explained in Section 5.2.3.4 ). In the following, the binary and mult i-class CSPs are further
investigated.
a) Binary case
In Fig. 5.13 and Fig. 5.14 , the releva nt common spatial patterns referring to the
selected alpha and beta frequency bands, are shown as s calp topogra phies considering
participant P4 . Fig. 5.13 shows the patterns for the alpha frequenc y band (8-14 Hz ) and the
patterns of the bet a band (16-20 Hz) are pres ented in Fig. 5.14 . Cle ar patterns are observed,
no e y e or muscle artifacts . The first components, with eigenvalues l arger than 0.5, refer to a
maximization of the va riance for the first class and a mi nimization of the variance fo r the
second class. On the o pposite, t he last compo nents with an eigenvalue lower than 0.5
repre s ents the second class max imization of the variance and in the same time first class
minimization of the variance. Pa rticipant P4 chosen for visualiz ation showed hi g hest
classification a ccuracy (77% accurac y fo r ST-DT, 92% for NT -DT and 80% for NT- ST
discrimination) and th e best behavioral response (46.66% perfect runs out of 15, with an
average behavioral ratio considering all conditions of 0.04 708). The c omponents which
maximize the varianc e of no-processin g level are characterized b y diffused activation
patterns at different scalp locations relating to random activity, in co mparison with the
components which max imize the variance of sh allow and deep pro cesses, which illustrate
activity in the central , te mporal or parietal area, with increased or de creased variance (green
or purple). The color coding and si gn of the activation patterns are not relevant in this
context. I n general, more re levant c omponents are detected in the case of discriminating a
higher level of processing which involves more activity, in comparison with lower lev el or
no-proce ssin g discrimination cases, where less sources of activity are detected.

Chapter V. Investiga tin g the neural corre l ates of cog nitive processing level s
90

Fig. 5.13 Binary CSP analysis (patterns) for the alpha frequency band (8-14Hz ) after SSD
considering NT-ST, NT -DT, ST-DT class pairs for each condition ( Par ticipant P4 ) . The
components with an eige nvalue less than 0.5 maximize the fi rst class variance and the others
greater than 0.5 maximize the second class variance, and were computed for the fi rst fold of
cross-validation, on the 350-2000ms interval. (Figure taken from Nicolae et al., 2017a , with
permission from Springer.)

5.3 Findings
91

Fig. 5.14 Binary CSP with SSD patterns for the beta frequency band ( 16-2 0 Hz ) considering
all conditions and classification pairs (Participant P4 ). The components represent the first
fold of cross -validation, computed on the 350-2000ms interval. ( Figure taken from Nicolae et
al., 2017a , with permission from Springer.)
b) Multi-class case
Apart from the two class CSP discrimination, we can further investigate the spatial
patterns of neural activit y spe cific to each class, considering the multi -case appro ach, a s
explained in the J AD method (Section 3.2.2.2.4.2 ). Tw o components ( m = 2) hav e been
chosen for each of the th ree class es , r epre s ented i n the first six CSP components given b y th e
highest eigenvalues. These patterns are pres ented in Fig. 5.15 for the alpha band (8-14 Hz)
and in Figure 5.16 for the beta band (16-20 Hz).
Considering the no pro cessing class , no particularly localized activit y is observed,
resembling for example mind wandering and attention as a ba ckground process. On the oth er

Chapter V. Investiga tin g the neural correlates of cognitive processing l evels
92
hand, the shallow processing compon ents consider frontal and pa rietal activations referr in g to
attention and processing of the sti muli. The deep processing c omponents involve
participations from different regions (frontal, temporal, central, parietal), co rre spondin g to the
type of cognitive process. For example, the de ep processing compone nts of the memor y
condition show more v ariance in the ante-frontal, fronto-central, ri ght temporal, cent ro -
parietal, and parietal areas corresponding to attenti on, short -term memory , memor y (non-
verbal), object reco g nition, visual association and organization processes, in accorda n ce. The
CSPs for the language condition relate to higher va riance in the front o-central, centro-
parietal, parietal and parietal-occipital areas, most li kely relating to language, working
memory and sequential thinking, non-verbal me mory and reasoning pro cesses. For visual
imagination, pronounced activations are disclosed in the ce nt ral, centro-p arietal, left tempo-
parietal and p arie tal re g ions corresponding to spatial representations and constructions,
recognition, short-term memory, picture ima ge s, cognitive reasoning, and im agination
mechanisms. (Dohrman n, 1983 ; Kolb and Whishaw, 1980 ; Lezak, 1983 ; Netter, 1983 ;
Kandel et al., 2000 ; Thompsn and Thompson, 2003 ; Kolak et al., 2006 ; Lloy d, 2007 ; Carter,
2014 ; Dubin, 2017 ).

Fig. 5.15 Multi-case CSP wit h SSD patterns of the 8-14Hz frequency ba nd considering the
NT, ST and DT c lasses for each condition (Participant P4 ). The two c omponents for e ach
class are presented on rows, w ith the corresponding eigenvalue. Th e components w ere
computed for the first fold of cross-validation.

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