COMPUTER SCIENCE AND INFORMATION TECHNOLOGY VOLUME: 12 |NUMBER: 5 |2014 |DECEMBER
Simila i y Analysis o EEG Da a Based on Sel
O ganizing Map Neu al Ne wo k
Ib ahim Salem JAHAN1, Michal PRILEPOK2, Vacla SNASEL2, Ma ek PENHAKER3
1Depa men o Compu e Science, Facul y o Elec ical Enginee ing and Compu e Science,
VSB–Technical Uni e si y o Os a a, 17. lis opadu 15, 708 33, Os a a, Czech Republic
2Depa men o Compu e Science, Facul y o Elec ical Enginee ing and Compu e Science, IT4 Inno a ions,
Eu opean Cen e o Excellence VSB–Technical Uni e si y o Os a a,
17. lis opadu 15, 708 33, Os a a, Czech Republic
3Depa men o Cybe ne ics and Biomedical Enginee ing, Facul y o Elec ical Enginee ing and Compu e
Science, VSB–Technical Uni e si y o Os a a, 17. lis opadu 15, 708 33, Os a a, Czech Republic
[email protected], mic[email p o ec ed], [email p o ec ed], [email p o ec ed]
Abs ac . The Elec oencephalog aphy (EEG) is he
eco ding o elec ical ac i i y along he scalp. This
eco ded da a a e e y complex. EEG has a big ole in
se e al applica ions such as in he diagnosis o human
b ain diseases and epilepsy. Also, we can use he EEG
signals o con ol an ex e nal de ice ia B ain Com-
pu e In e ace (BCI) by ou mind. The e a e many
algo i hms o analyse he eco ded EEG da a, bu i
s ill emains one o he big challenges in he wo ld. In
his a icle, we ex ended ou p e ious p oposed me hod.
Ou ex ended me hod uses Sel -o ganizing Map (SOM)
as an EEG da a classi ie . The p oposed me hod we can
di ide in ollowing s eps: cap u ing EEG aw da a om
he senso s, applying il e s on his da a, we will use he
equencies in he ange om 0.5 Hz o 60 Hz, smoo h-
ing he da a wi h 15- h o de o Polynomial Cu e
Fi ing, con e ing il e ed da a in o ex using Tu le
G aphic, Lempel-Zi complexi y o measu ing simila -
i y be ween wo EEG da a ials and Sel -O ganizing
Map Neu al Ne wo k as a inal classi ie s. The expe -
imen esul s show ha ou model is able o de ec up
o 96 % inge mo emen s co ec ly.
Keywo ds
EEG da a, elec oencephalog aph, polynomial
cu e i ing, SOM, unsupe ised lea ning.
1. In oduc ion
To use he EEG signals o communica e be ween he
human b ain and an ex e nal de ice becomes one o he
cu en big challenges in his esea ch ield. When we
a e looking on he EEG da a o di e en men al asks,
hey seem o be iden ical, bu in de ails hey a e di -
e en . They con ain di e en in o ma ion. So we need
o ind an e icien me hod o algo i hm o de ec hese
di e ences be ween di e en men al asks and be able
o dis inguish be ween hem. When we a e able o dis-
inguish be ween wo o mo e a ious men al asks wi h
a sa is ying success a e, we can ans o m e e y men al
ask o a con ol command o an ex e nal de ice, such
as p os hesis and wheelchai . The EEG signals clas-
si ica ion was p esen ed by se e al esea che s using
a ious echniques, o example Non-nega i e ma ix
ac o iza ion (NMF) [1] as a one o e icien me hods
o ecognize human men al asks.
2. Rela ed Wo ks
In his ield, we can ind many pape s which a e o-
cused on EEG da a p ocessing. In his sec ion, we
p esen a b ie o e iew o some me hods which a e e-
la ed o ou a icle. Zhang e al. applied Polynomial
Cu e Fi ing (PCF) o imp o e Image Quali y in Elec-
ical Impedance Tomog aphy (EIT). The expe imen s
on he 2D model con i med he imp o ing quali y o
he econs uc ed image; also PCF can be used o im-
p o e econs uc ed image quali y in 3D EIT [2]. Ta-
ade and Ka i, compa ed Au o Reg essi e In eg a ed
Mo ing A e age (ARIMA), A i icial Neu al Ne wo k
(ANN) and polynomial cu e i ing (PCF) o wind
speed p edic ion. Thei esul s showed, ha ARIMA
is be e han o he me hods [3]. Kang and Lee p e-
sen ed algo i hm o compensa ing ne wo k delays in
a sma ac ua o based on he Lag ange Polynomial
cu e i ing. Thei expe imen al esul s showed, ha
his me hod can be used e ec i ely o message de-
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lay o sma ac ua o [4]. Zhang e al. hey p oposed
a me hod based on he polynomial cu e i ing algo-
i hm o p ocess he ligh es ing da a and hei esul
showed ha he p oposed me hod can e icien ly au o-
ma ically elimina e he ou lie e o s [5]. Jishui e al.
hey p oposed mul i-dimensional uzzy easoning algo-
i hm o op imize he calcula ion p ocess and imp o e
he i ing cu e speed and accu acy o NC Machining
G aphics. Thei esul s showed ha his me hod has
a sho ime compu a ion and imp o ed he i ing al-
go i hm and i ing p ecision and is sui able o cu e
i ing o NC machine [6]. Shang e al. hey applied
cu e i ing o phase calib a ion algo i hm using e o
ol age da a om sa elli e acking. The esul showed
ha we could use his me hod in monopulse acking,
which does no need o build he sou ce and we can
use only he e o ol age [7]. Jiang e al. hey p o-
posed me hod o he aul loca ion de ec ion in elec i-
cal cables based on la coe icien compu a ion. Cable
aul loca ion analysis is combined wi h wa ele ans-
o m and i ing cu e echnique. This pape p o ed
ha he p oposed me hod educes de ia ion o singu-
la i y de ec ion and imp o es he aul loca ion p eci-
sion [8]. Yixu Song e al. p oposed new me hod based
on cu e i ing echnique combined wi h he clus e ing
algo i hm o s o e he da a s eam. The expe imen e-
sul s o his me hod depic he bes comp ession a io
and i ing accu acy [9]. Zhang and Liu applied cu e
i ing echnique me hod o de ec he disloca ion de-
ec in polysilicon slices. They compa ed wo me hods
o cu e i ing, quad a ic cu e i ing and Gaussian
cu e i ing. Thei esul s showed ha he quad a ic
cu e i ing is e ec i e and accu a e o de ec he dis-
loca ion de ec in polysilicon slices [10]. Nisha e al.
hey applied Cu e Fi ing Technique o Pulse Wid h
Modula ion (PWM) swi ching angles o h ee phase
in e e s. The esul s p o ed ha he quad a ic cu e
i ing is su icien o de e mine he op imal swi ching
angles in compa ison o he cubic cu e i ing [11].
Dohnalek e al. hey applied Non-nega i e ma ix ac-
o iza ion on EEG da a o ind EEG pa e n ma ching
and also hey used sho ime Fou ie ans o m o p e-
p ocess EEG da a, Cosine Simila i y Measu e o ind
simila i y in he EEG da a. The esul s o his pape
showed ha his me hod is sui able o implemen a ion
on g aphics p ocesso s in eal-wo ld and eal- ime ap-
plica ions [12]. Apoy e al. applied he LZ complexi y
o analysis biomedical signal, hey conclude ha he
LZ complexi y is use ul o es ima e he bandwid h o
andom p ocesses and in ha monic a iabili y in liken-
pe iodic signal [13]. Hu e al. hey compa ed he
LZ complexi y wi h co ela ion en opy o epilep ic
seizu e de ec ion in EEG da a. Thei esul concludes
ha he LZ complexi y is be e han he co ela ion
en opy in he de ec ing epilep ic seizu es p oblem [14].
Guo e al. hey ha e used he LZ complexi y and dy-
namic p og amming algo i hm o analyse and measu e
he simila i y o DNA sequences. The expe imen e-
sul s p o ed he alidi y o hei p oposed me hod [15].
Noshadi e al. hey ha e combined Empi ical mode
decomposi ion (EMD) and Lempel Zi (LZ) complex-
i y o dis inguish be ween human men al asks. Thei
p oposed me hod eached in a e age 92.46 % in men-
al ask classi ica ion [16]. Ling e al. hey used he
LZ complexi y o analyse he EEG ime se ies du ing
di e en sleep s a es o eigh heal hy adul s. The ex-
pe imen esul showed ha he p oposed algo i hm is
e icien and sui able o disc imina e he sleep s a es
o he b ain, bu has some losses o da a in he da a
p ep ocessing phase [17].
3. In oduc ion o EEG
The Elec oencephalog aph (EEG) is measu ing and
eco ding he di e ences o he ol age om wo si es
on he scalp o e ime. The i s eco ding o elec-
ical ac i i y o he human b ain was made by B ege
(Be ge , 1929), when he explained his me hod o mea-
su ing he elec ical ac i i y o he human b ain on he
scalp. The EEG signal in common has ampli ude om
a ew mic o ol s up o 100 µV wi h equency in he
ange om 0.5 o 40 Hz [18]. We can eco d EEG
signal be ween wo ac i e elec odes, bipola eco d-
ing, o be ween one ac i e elec ode and a e e ence
elec ode, monopola eco ding [19]. Elec oencephalo-
g aph (EEG) is gene ally used in he diagnosis o b ain
diseases and epilepsy, esea ch a eas, due o he alu-
able in o ma ion ha con ey by EEG signal [18].
3.1. 10–20 In e na ional Sys em
EEG eco ding is made by pu ing se o senso s on
he human b ain acco ding o 10–20 In e na ional Sys-
em as Fig. 1. The 10–20 in e na ional EEG Elec ode
placemen is he in e na ional sys em o de e mining
he EEG elec odes loca ions on he human skull. I
con ains 21 EEG elec odes wi hou he ea lobe elec-
odes ha called A1 connec ed o he le ea lobe and
A2 connec ed o he igh ea lobe. These elec odes
a e no mally used as e e ence elec odes [20], The le -
e s F, T, C, P, and O s and o F on al, Tempo al,
Cen al, Pa ie al and Occipi al [21]. The elec odes
ha ha e e en numbe (2, 4, 6) a e placed on he igh
side o he skull. The elec odes wi h odd numbe s (1,
3, 5) a e placed on he le side o he skull and Z o
ze o on midline o he skull [20]. The 10 and 20 e-
e ed o he cu en dis ance be ween an elec ode o
o he elec ode, ei he 10 % o 20 % o he whole o
dis ance om igh side o he skull o he le side,
o om on o back o he skull. In some applica-
ions, we need mo e EEG elec odes. In his case we
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can pu some elec odes be ween he o iginal elec odes
acco ding o 10–20 sys em, as Fig. 1 [20].
3.2. EEG A i ac s
The EEG da a a e e y sensi i e and complica ed.
The e o e he EEG da a mus be clea om con ained
su ounding in e e ence o ge good and eliable e-
sul s.
The EEG signal no mally con ains noise and di e -
en kinds o in e ace signal (a i ac s). This noises
ei he in e nal signals a e p oduced by subjec himsel
such as elec ical ac i i y o hea , pulse, body mo e-
men , pe spi a ion, eye blinking, eyes mo emen , mus-
cles ac i i ies, o ex e nal signals p oduced ou o he
subjec , o example 50/60 Hz om elec ical powe
supply, EEG de ices, elec onic elemen s [20], [21] and
e c.
Fig. 1: 10–20 In e na ional Sys em o EEG Elec odes Place-
men he Nasion is he place be ween he o ehead and
nose, Inion is he ju a back o he skull [20].
Fig. 2: EEG Signal con amina ed by powe line in e e ence
[22].
In EEG signal p ocessing ield, he emo ing o hese
noises and a i ac s om EEG signal is an impo an
opic [18]. Fo example, Fig. 2 show EEG signal con-
amina ed by powe line in e e ence, while Fig. 3 show
some EEG signal con amina ed by eye blinking a i-
ac . EEG mus be il e ed o ob ain clea EEG, wi h-
ou in e e ence and a i ac s, so ha da a become
eady o u he analysis. The noise signal and un-
wan ed signal mus be elimina ed o minimized om
EEG da a wi hou losing signi ican in o ma ion and
quali y ha embedded in EEG o ensu e an accu a e
and pe ec analysis and diagnosis o he EEG.
The e a e se e al echniques o il e ing EEG signal
such as con en ional il e s and adap i e il e s ha
ha e mo e e iciency han con en ional il e s o elim-
ina ion o he a i ac s om EEG, because EEG signal
and a i ac s ha e o e lapping spec a [23].
Fig. 3: EEG Signal con amina ed by eye blinking a i ac [22].
4. Tu le G aphics
Tu le g aphics (TG) is a e m in compu e g aphics
o a me hod o p og amming ec o g aphics using a
ela i e cu so posi ion ( he " u le") upon a Ca e-
sian plane. In he TG, we ha e a u le wi h a d aw-
ing pen on a compu e sc een. This u le mus e-
spond on a sequence o commands. The u le can be
con olled using hese basic commands: o wa d com-
mand, is mo ing he u le in on a ew numbe o
uni s, igh commands o a e u le in a clockwise di-
ec ion a ew numbe o deg ees. These commands can
be ex ended wi h o he mo e complica ed commands.
The back and le commands cause same mo emen as
o wa d and igh command, bu in he opposi e way.
The numbe o commands o de e mine how much o
mo e is called inpu commands, depending on he ap-
plica ion. When mo ing he u le acco ding o he
inpu commands, i lea es a ace, his ace ep esen
he desi ed objec [24] as a simple example in Fig. 4.
By This way we can ep esen and d aw he objec s,
om simple o complex objec s.
Using TG we con e ed EEG da a om nume ic al-
ues in o ex da a and p ocess hem as ex . This con-
e sion helps us o compa e wo EEG da a ial, wo
men al asks, such as inge mo emen [25].
E e y EEG ail is ep esen ed by sequence o
commands–mo e o wa d and u n le o igh .
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Fig. 4: A simple example o u le g aphic.
5. Compa ing Da a wi h he
LZ Complexi y
The Lempel-Zi (LZ) complexi y o sequences o i-
ni e leng h was sugges ed by Lempel and Zi [26]. I is
a non-pa ame ic, simple- o-calcula e measu e o com-
plexi y in a one-dimensional da a. The LZ complexi y
is ela ed o he numbe o dis inc subs ings and he
a e o hei ecu ence along he gi en sequence [27].
The la ge alues co espond o mo e complexi y in he
da a.
The compa ing o wo TG commands lis s is he
main ask o his a icle. The lis s a e compa ed each
o o he . The main p ope y o he compa ison is he
numbe o common sequences in bo h lis s. These se-
quences a e ob ained a e applying he LZ complexi y
o he TG commands lis . This numbe is ep esen ed
by he pa ame e in he ollowing o m Eq. (1), which
is a me ic o simila i y be ween wo u le commands
lis .
SM =sc
min(c1, c2),(1)
whe e sc - Coun o common LZ sequence in bo h com-
mand lis s, c1, c2- Coun o LZ sequence in i s o
second command lis .
This SM gi es a esul in he ange be ween 0 and
1. The 0 esul ells us ha his wo compa ed TG
commands lis ha e no hing common. They ha e he
highes di e ence. I he esul is equal o 1, he wo
compa ed TG commands lis a e same.
6. In e pola ion o he EEG
Da a
A e eco ding and il e ing o he eco ded EEG da a,
we apply polynomial cu e i ing o da a smoo hing.
The i ing will emo e noise and in e e ence om he
da a and i he da a end.
Conside he gene al o m o a polynomial i ing
cu e o o de j:
(x) = a0+a1x+a2x2+· · · +ajxj=
j
X
k=1
akxk.(2)
We minimized he o al e o o polynomial i ing
cu e wi h leas squa e app oach. The gene al exp es-
sion o any e o using he leas squa es app oach is:
e =X(dj)2,(3)
e =
n
X
i=1
(yi−(a0+
j
X
k=1
akxk))2,(4)
whe e n- is a coun o da a poin s in one mo e, i- is he
cu en da a poin being summed, j- is he polynomial
o de .
7. Sel -O ganizing Map (SOM)
Sel -O ganizing Map (SOM) is an unsupe ised lea n-
ing neu al ne wo k. The SOM in mos common used
o he clus e ing and isualiza ion o complex da a.
The SOM educes he da a dimension by p oduce map
usually in one o wo dimension in he ou pu ha
plo s simila i ies o da a oge he as Fig. 5. The SOM
is ained a e many o i e a ion in he aining phase
un il he map becomes s able a he ou pu . This map
is gene a ed in he aining phase and used in he es -
ing phase o es ima e in which g oup can belong he
es inpu , while in o he ne wo k ypes, Backp opa-
ga ion ne wo ks, is he a ge ou pu used o ain he
ne wo k [28].
7.1. SOM Algo i hm
The SOM lea ning we can di ide in ollowing s eps:
•Ini ializing weigh ec o s wi h small andom al-
ues.
•Choosing andom ec o om he aining se and
p esen o ne wo k.
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Fig. 5: SOM S uc u e.
•Finding winning neu on which has minimum dis-
ance om da a inpu based on speci ic c i e ion,
o SOM usually using Euclidean dis ance o mea-
su e he dis ance be ween da a inpu and neu ons
as Eq. (5).
D(X, W ) = p(x1−w1)2+· · · + (xn−wn)2.(5)
The winning neu on is called Bes Ma ching Uni
(BMU).
•Calcula e he adius o he neighbo hood o BMU
using Eq. (6).
( ) = 0e(− /T ),(6)
whe e ( ) - is adius o he neighbo hood, 0-
is he adius o he map, T- ime cons an , -
Cu en i e a ion.
•Any nodes ound wi hin he adius o BMU mus
be upda e, his means mo e he BMU and i s
neighbo hood nodes owa d da a inpu as Fig. 6
using he Eq. (7).
W( + 1) = W( ) + β( )h( )(X( )−W( )),(7)
β( ) = β0e(− /T ),(8)
h( ) = e(−(dis ance om BMU)2/2 2( )),(9)
whe e β( )- Lea ning a e, h( ) - neighbo hood
unc ion.
•Repea ing he s eps om s ep 2 o s ep 5 o many
i e a ions un il he map a ou pu becomes s able
[28].
8. P oposed Me hod
The p oposed me hod is using Neu al ne wo k Unsu-
pe ised lea ning o classi y EEG da a. Ou model was
es ed on EEG da a o de ec index inge mo emen .
We made ou p oposed me hod as ollowing: il e and
he smoo h EEG da a (T aining da a se ) using 15 h
Fig. 6: Upda e o Winne Neu on (BMU) and i s neighbo s,
his means mo e hem owa ds da a inpu indica ed
wi h X. The solid and do ed line co espond o he
s a us be o e and a e upda e espec i ely.
o de polynomial cu e i ing, a e ha we con e
he smoo hed EEG da a in o ex o m using u le
g aphic. The LZ complexi y we used o compa e wo
TG commands lis s and assign he ype o mo emen o
p ocessed da a ail [29]. This was done o e e y sen-
so o p ocessed ial. We made a ec o V wi h dimen-
sion 8, 7 channels and one da a ype class. This ec o
V is used o ain he Sel -O ganizing Map (SOM) neu-
al ne wo k wi h dimension o 5×5nodes o p oduce
he map. When he aining is inished, he map o
ou pu becomes s able. In he es ing phase we used
o he EEG da a (Tes ing da a se ) o es he ne wo k
as depic in expe imen scheme in Fig. 7.
8.1. EEG Da a
The EEG Da a used in his expe imen was eco ded
in ou labo a o y. In ou expe imen we used se en
EEG channels, which we e selec ed by ou Biomedical
Depa men . These se en channels a e able o cap u e
mos inge mo emen da a. The eco ded signals con-
ain mo emen s o one index inge . We eco ded EEG
Da a om ou di e en subjec s. E e y one o hem
pe o med a p ess o a bu on wi h le index inge . We
used 320 eco ded inge mo emen s, and 320 eco ded
ials wi hou inge mo emen . Fo e e y ask we used
576 ials o he aining se (288 ials wi h mo emen
and 288 wi hou mo emen ) and 64 ials o es ing se
(32 ials wi h mo emen and 32 wi hou mo emen ).
The sampling a e was se o 256 Hz, and he band-pass
il e was se o 0.5 Hz o 60 Hz o emo e unwan ed
equencies and noises.
While ex ac ing he ask da a om cap u ed EEG
da a, we added be o e and a e e e y ask a ime in-
e al 0.3 second.
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Fig. 7: The schema ic diag am o p oposed me hod. Black and
blue lines ep esen aining phase. Red do ed lines
ep esen es ing phase.
8.2. Expe imen Resul s
To ain and es ou model we used k- old C oss-
alida ion echnique wi h k= 10. The EEG da a se is
di ided in o 10 sub-se s, o olds and he expe imen s
a e epea ed o 10 imes. The ecogni ion esul s o
inge mo emen s a e lis ed in Tab. 1 and he esul s
o ials wi hou inge mo emen a e in Tab. 2.
Tab. 1: Finge mo emen esul s.
k- old Iden i ied Mis ake Iden i ied
1 93.750 % 6.250 %
2 100.00 % 0.000 %
3 93.750 % 6.250 %
4 93.750 % 6.250 %
5 96.875 % 3.125 %
6 100.00 % 0.000 %
7 100.00 % 0.000 %
8 96.875 % 3.125 %
9 90.625 % 9.375 %
10 100.00 % 0.000 %
A g 96.563 % 3.438 %
The p oposed model is able o de ec index inge
mo emen in he ange be ween 90.6 % and 100.00 %.
The de ec ion a e o ials wi hou inge mo emen
a ied in he ange be ween 90.6 % and 100.00 %.
The Table 3 show he pe cen age o o al iden i ied
and misiden i ied ials in ou expe imen . The p o-
posed model is able o de ec in a e age 96.250 % o
inge mo emen ials co ec ly. The o al a e age o
misiden i ied ials is abou 3.750 %. The a e age i-
nal quan iza ion e o is 0.6556, and he a e age inal
opog aphic e o is 0.007.
9. Conclusion
This expe imen shows he abili y o ind and ecog-
nize di e en men al ask in EEG da a. This helps us
Tab. 2: Wi hou inge mo emen esul s.
k- old Iden i ied Mis ake Iden i ied
1 93.750 % 6.250 %
2 90.625 % 9.375 %
3 100.00 % 0.000 %
4 96.875 % 3.125 %
5 100.00 % 0.000 %
6 90.625 % 9.375 %
7 93.750 % 6.250 %
8 96.875 % 3.125 %
9 96.875 % 3.125 %
10 100.00 % 0.000 %
A g 95.938 % 4.063 %
Fig. 8: U ma ix o k- old 1.
Fig. 9: SOM o k- old 1, Red Nodes ep esen mo emen i-
als and G een Nodes ep esen he wi hou Mo emen
ials.
o unde s and he aluable in o ma ion which is hid-
den in he EEG da a. Ou app oach is able o de-
cide be ween wo asks, p essed bu on wi h index in-
ge and eleased bu on. We used only se en selec ed
elec odes. This coun o elec odes is enough o cap-
u e good EEG da a o mo emen . As a i s s ep, we
used a band pass il e , o il e ou wan ed equencies
which a e use ul o inge mo emen de ec ion. Ou
c
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Tab. 3: E alua ion o he esul s.
k- old To al
Iden i ied
To al
Misiden i ied
Final
Quan iza ion
E o
Final
Topog aphic
E o
1 93.750 % 6.250 % 0.658 0.007
2 95.313 % 4.688 % 0.664 0.000
3 96.875 % 3.125 % 0.653 0.008
4 95.313 % 4.688 % 0.655 0.008
5 98.438 % 1.563 % 0.658 0.017
6 95.313 % 4.688 % 0.674 0.013
7 96.875 % 3.125 % 0.653 0.003
8 96.875 % 3.125 % 0.654 0.008
9 93.750 % 6.250 % 0.638 0.007
10 100.00 % 00.00 % 0.649 0.000
A g 96.250 % 3.750 % 0.6556 0.007
Fig. 10: U ma ix o k- old 10.
Fig. 11: SOM o k- old 10, Red Nodes ep esen mo emen i-
als and G een Nodes ep esen he wi hou Mo emen
ials.
sugges ed app oach is using high o de polynomial i -
ing cu e o noise and in e e ence elimina ion, u le
g aphic o con e il e ed da a om numbe s in o ex ,
Lempel-Zi complexi y o compa e wo da a ials a
Sel -O ganizing maps as a classi ie .
The da a ial we e cu 0.3 second be o e he men-
al ask began and 0.3 second a e . In ou expe i-
men , we il e ed da a wi h polynomial i ing wi h o -
de 15. This o de is enough o i da a end and
emo e unwan ed noise and in e e ence su ounding
en i onmen . As a classi ie , we chose SOM wi h map
dimension 5×5neu ons. The es ing ec o was as-
signed o clus e using BMU.
Ou model was able o de ec on inge mo emen
as a e age abou 96.56 %, he lowes a e we eached
was 90.625 %, and he highes was 100.00 %. Fo a
ial wi hou inge mo emen he a e age success ul
a e is abou 95.93 %, he lowes a e is 90.625 % and
he maximal is a 100.00 %. The a e age o bo h o
de ec s on inge mo emen and wi hou inge mo e-
men abou 96 %. In he u u e, we will con inue wi h
o he publica ion ega ded o es ing o he EEG da a
o modi ying ou model o imp o e he esul o EEG
da a ecogni ion and inc ease he speed o ou model.
Acknowledgmen
This wo k was suppo ed by he Eu opean Regional
De elopmen Fund in he IT4Inno a ions Cen e o
Excellence p ojec (CZ.1.05/1.1.00/02.0070) and by
P ojec SP2014/110, Pa allel p ocessing o Big da a,
o he S uden G an Sys em, VSB–Technical Uni e -
si y o Os a a.
This pape has been elabo a ed in he amewo k o
he p ojec „Suppo esea ch and de elopmen in he
Mo a ian-Silesian Region 2013 DT 1 - In e na ional
esea ch eams“(RRC/05/2013). Financed om he
budge o he Mo a ian-Silesian Region. The pape
has been elabo a ed in he amewo k o BIOM ( eg.
no. CZ.1.07/2.3.00/20.0073). The wo k and he con-
ibu ions we e suppo ed by he p ojec SP2014/194
’Biomedicinske inzeny ske sys emy X’.
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Abou Au ho s
Ib ahim Salem JAHAN was bo n in Mis a a Libya
in 1974. He ecei ed his M.Sc. om VSB–Technical
Uni e si y o Os a a in 2010 a Facul y o Elec ical
Enginee ing and Compu e science in Measu emen
and con ol Enginee ing Depa men . Now he Ph.D.
S uden in VSB–Technical Uni e si y o Os a a, in
Compu e Science Depa men .
Michal PRILEPOK was bo n in Dolny Kubin,
Slo ak Republic in 1985. He ecei ed his M.Sc.
om VSB–Technical Uni e si y o Os a a in 2010
a Facul y o Elec ical Enginee ing and Compu e
science in Compu e Science and Technology. Since
2010 is a Ph.D. s uden in VSB–Technical Uni e si y
o Os a a, in Compu e Science Depa men .
Vacla SNASEL was bo n in Olomouc. His
esea ch and de elopmen expe ience includes o e
30 yea s in he Indus y and Academia. He wo ks
in a mul i-disciplina y en i onmen in ol ing a i-
icial in elligence, mul idimensional da a indexing,
social ne wo k, o mal concep analysis, in o ma ion
e ie al, seman ic web, knowledge managemen , da a
comp ession, machine in elligence, neu al ne wo k,
web in elligence, na u e and biologically inspi ed
compu ing, da a mining, and applied o a ious
eal wo ld p oblems. He has gi en mo e han 16
plena y lec u es and con e ence u o ials in hese
a eas. He has au ho ed/co-au ho ed se e al e e eed
jou nal/con e ence pape s and book chap e s. He has
published mo e han 450 pape s.
Ma ek PENHAKER was bo n in 1972. He
inished M.Sc. in 1996 a Facul y o Elec ical
Enginee ing and Compu e science in specializa ion
Measu emen and Con ol in Biomedicine a VSB–
Technical Uni e si y o Os a a, Czech Republic. He
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