1 © 201 8 IO P Pub l is hi ng L td Pri nte d i n th e UK
Journal of Neural Engineering
Implicit r ele v ance feedbac k fr om
electr oencephalogr aph y and e ye
tr ac king in ima g e sear c h
J an-Eik eGolenia 1 , 2 , MarkusAW enzel 1 , 2 , MihailBogojeski 1
and BenjaminBlank er tz 1
1 Fachgebiet Neurotechnologie, T echnische Uni versit ä t Berlin, Marchstr . 23, 10587 Berlin, Germany
2 Equal contrib utions.
E-mail: [email protected] , [email protected] .de , [email protected] and
[email protected]
Recei ved 14 April 2016, revised 25 October 2017
Accepted for publication 10 Nov ember 2017
Published 24 January 2018
Abstr act
Objective. Methods from brain–computer interf acing (BCI) open a direct access to the mental
processes of computer users, which of fers particular benefits in comparison to standard
methods for inferring user -related information. The signals can be recorded unobtrusi vely in
the background, which circumvents the time-consuming and distracting need for the users to
gi v e explicit feedback to questions concerning the indi vidual interest. The obtained implicit
information makes it possible to create dynamic user interest profiles in real-time, that
can be taken into account by no v el types of adapti ve, personalised softw are. In the present
study , the potential of implicit rele v ance feedback from electroencephalography (EEG) and
eye tracking w as e xplored with a demonstrator application that simulated an image search
engine. Appr oach. The participants of the study queried for ambiguous search terms, ha ving
in mind one of the two possible interpretations of the respecti ve term. Subsequently , they
vie wed dif ferent images arranged in a grid that were related to the query . The ambiguity
of the underspecified search term was resolv ed with implicit information present in the
recorded signals. For this purpose, feature v ectors were e xtracted from the signals and used
by multi v ariate classifiers that estimated the intended interpretation of the ambiguous query .
Main r esult. The intended interpretation w as inferred correctly from a combination of EEG
and eye tracking signals in 86% of the cases on a verage. Information pro vided by the two
measurement modalities turned out to be complementary . Significance. It w as demonstrated
that BCI methods can extract implicit user -related information in a setting of human-computer
interaction. Nov elties of the study are the implicit online feedback from EEG and e ye tracking,
the approximation to a realistic use case in a simulation, and the presentation of a lar ge set of
photographies that had to be interpreted with respect to the content.
K e ywords: e ye fixation related potentials, implicit rele v ance feedback, e ye tracking,
brain-computer interfacing, electroencephalography
(Some figuresmay appear in colour only in the online journal)
J-E Golenia etal
Implicit rele v ance feedback from electroencephalography and eye tracking in image search
Printed in the UK
026002
JNEIEZ
© 2018 IOP Publishing Ltd
15
J. Neural Eng.
JNE
1741-2552
10.1088/1741-2552/aa9999
P aper
2
Journal of Neural Engineering
IOP
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J. N eur al E ng . 15 (201 8) 02 60 02 (1 0pp)

J-E Golenia etal
2
1 . Introduction
Signals from the brain may contain implicit information about
the users of computers, which can potentially be decoded
with methods from brain–computer interfacing (BCI) [ 1 – 4 ].
Such a direct access to the mental processes of the users of fers
particular benefits in comparison to standard methods for the
inference of user -related information, e.g. asking the user for
explicit feedback, or observing the user ’ s interaction with the
de vice. Physiological signals can be recorded unobtrusi vely in
the background, and their analysis would circumv ent the time-
consuming and distracting need for the user to gi v e explicit
feedback to questions concerning the indi vidual interest, as
well as a possible response bias. The obtained implicit infor-
mation could augment standard input de vices (e.g. computer
mouse and ke yboard) for the interaction between human and
machine.
Research on BCI has sho wn that humans can v olitionally
generate ‘ neural signatures ’ that can be detected in the elec-
troencephalogram (EEG) with pattern recognition methods
in real-time. The extracted information can be translated into
a signal serving for control or communication [ 5 – 9 ]. Some
BCI methods exploit the phenomenon that stimuli of interest,
which are flashed in a stimulus sequence, elicit a detectable
attention-related neural response [ 10 – 13 ]. Combining this
BCI technique with eye tracking mak es it possible to infer
the subjecti v e rele v ance of the single elements of the visual
surrounding [ 14 – 21 ].
The present study demonstrates that it is possible to decode
from EEG and eye tracking signals which images were sub-
jecti v ely rele v ant for the user of a simulated web image search
engine (see ‘ Flickr ’ or ‘ Google Images ’ ). The resulting rel-
e v ance map of the computer screen, where numerous images
were displayed at the same time in a grid, made it possible to
characterise the current interest of the indi vidual user . Implicit
rele v ance information can be aggre gated in dynamic user
interest profiles, that could be taken into account by no v el
types of adapti v e, personalised software. This potential is
explored here with a demonstrator application that infers the
user interest online from implicit information hidden in the
signals. Nov elties of the study are the implicit online feed-
back from a combination of EEG and eye tracking signals,
the approximation to a realistic use case in a simulation, and
the presentation of a lar ge set of photographies that had to be
interpreted with respect to the content (which goes beyond the
mere recognition of pre viously kno wn simple stimuli that are
typical for BCI paradigms based on e v ent-related potentials).
The demonstrator is not considered to be a final application
of its o wn right, b ut may be an important step to wards future
applications that are informed by the insights gained.
The presented nov el approach may sho w promise in light
of the increasing interest of customers and lar ge technology
companies in wearable physiological sensors [ 22 ] and recently
de v eloped, deployable e ye tracking and EEG systems, which
will make the signal acquisition during daily life more and
more feasible — in contrast to the b ulky , e xpensi v e, incon ven-
ient, and stationary equipment of the past. Examples of the
technological innov ations are af fordable eye track ers [ 23 ] and
mobile EEG systems [ 24 – 26 ] with gel-free [ 27 – 30 ], minia-
turised [ 31 ] electrodes that can be placed hardly visible in/
on/around the ear [ 32 – 36 ]. Moreo ver , in-ear headphones with
dif ferent physiological sensors including EEG, which connect
with a smartphone, are under de v elopment (e.g. ‘ The A ware ’
from ‘ United Sciences ’ , Atlanta, USA).
2. Methods
2.1 . Experiment al design
The participants of the study queried for ambiguous terms in
a simulated image search engine, and vie wed dif ferent images
that were related to the respecti v e search term. During image
vie wing, the EEG was recorded and the e ye mo vements were
tracked. Feature v ectors were e xtracted from the signals in
order to train a classifier that estimated the intended interpre-
tation of the ambiguous search term. First, the participants
were asked to choose one of tw o possible interpretations
(like ‘ animal-nature-wildlife ’ versus ‘ baseball-ball-sports ’ )
of an ambiguous search term (here ‘ bat ’ ). Then, they vie wed
24 square images arranged in a four -times-six grid on the
screen that were related to either one or the other meaning
of the query (see figure 1 ; non-square images were cropped).
Finally , they were ask ed to report the number of the pictures
belonging the chosen category and got feedback on whether
their response was correct. This procedure was repeated 154
times with dif ferent ambiguous search terms. Further e xam-
ples of the queries are ‘ jam ’ with the possible interpreta-
tions ‘ cream-tea-scone ’ versus ‘ music-guitar -band ’ , ‘ deck ’
( ‘ ship-sea-boat ’ versus ‘ skateboard-skate-board ’ ), and ‘ tick ’
( ‘ macro-insect-b ug ’ versus ‘ time-clock-tock ’ ). The partici-
pants were instructed to quickly skim the images instead of
prioritizing the correct accomplishment of the counting task,
assuming that this beha viour is typical when bro wsing image
search results. Before the appearance of the image mosaic, a
fixation cross directed the gaze to the upper left corner of the
screen. Each picture sho wn in the image mosaic w as picked
randomly from one of the two gi ven cate gories with a prob-
ability of
p = 11 / 24

. In addition, fe w ‘ odd ’ pictures, which
were not related to the query , were displayed with a proba-
bility of
p = 2 / 24

. The odd pictures were randomly selected
from the remainder of the image collection.
2.2. Experiment al stimuli
All pictures were obtained from Flickr [ 37 ], a service for
sharing pictures aimed at amateur and professional photog-
raphers. Flickr provides access to a lar ge collection of user
annotated pictures via an application programming interface
( ‘ API ’ ; [ 38 ]). Flickr clusters the images into categories that
contain images with similar content according to the user
annotations (tags). These clusters can be accessed via the API
with the ‘ cluster search ’ function. Called with a single search
term, the function returns up to four clusters. Each cluster is
described by a list of tags and named after the first three tags.
Se v eral lists of homonyms (e.g. [ 39 ]) serv ed as query terms
for the cluster search function, and a collection of 63 110
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J-E Golenia etal
3
images related to 936 ambiguous terms was do wnloaded.
Search terms were picked that generated tw o clusters with
more than 18 pictures each that could be clearly associated
with the name of the respecti v e cluster . A manual revie w was
necessary , because many pictures were hardly in an y relation
to the cluster name or query term.
Ambiguity was rarely the result of le xical homon ymy , but
more often due to underspecified search queries. The search
term ‘ filter ’ resulted, for instance, in images of coffee filters,
in pictures of filter lenses made of glass and in photographies
processed by dif ferent digital filters. The two cate gories were
illustrated for the participant by the first three tags and one
example picture per cluster (see section 2.1 and figure 1 ) .
Some categories could be easily distinguished, others not.
For instance, the cate gories ‘ hyacinth-flo wer-blue ’ and ‘ fruit-
green-macro ’ of the search term ‘ grape ’ could be easily
discerned. The former consisted of close-up photographies
of blue hyacinth flo wers in a grape shaped form, the latter
contained grapes and other fruits that were ne v er blue. In
contrast, it was dif ficult to distinguish the categories ‘ paint-
art-painting ’ and ‘ makeup-e yeshado w-cosmetics ’ of the
search term ‘ palette ’ , because the images of both categories
depicted colour palettes, that contained either make-up or
paint for drawing.
2.3. Dat a acquisition
Fourteen persons with normal vision and no report of e ye or
neurological diseases participated in the e xperiments. The
age of the five female and nine male subjects ranged from
22 to 33 yr with a mean age of 27.7 yr (standard de viation:
2.96). The first subject vie wed 123 result pages and all others
154 result pages. One recording session included gi ving an
informed written consent to take part in the study , vision tests
for eye dominance, preparation of the sensors, e ye track er
calibration and v alidation, introduction to the task and the
main experiment (with a duration of about 1.5 h). The study
was appro v ed by the ethics committee of the Department of
Psychology and Er gonomics of the T echnische Uni versit ä t
Berlin (application number BL_03_20150109).
The participant sat at a distance of 60 cm in front of a
comp uter screen and entered the number of the counted tar get
pictures with a ke yboard. Physiological signals were recorded
with two amplifiers with 62 acti ve EEG electrodes (BrainAmp,
ActiCap, BrainProducts, Munich, Germany; sampling fre-
quency of 1000 Hz) and one acti ve electrode for electrooc-
ulography (EOG). An eye track er (RED 250, SensoMotoric
Instruments, T elto w , Germany; sampling frequenc y of
250 Hz) was attached to the screen. A chin rest gav e orienta-
tion for a stable position of the head. The screen had a resolu-
tion of 1680 pixels × 1050 pixels, a size of 47.2 cm × 29.6 cm
and subtended a visual angle of
38.2 ◦

in horizontal and
26.3 ◦

in vertical direction.
EEG was acquired and analysed with W yrm and Mushu
[ 40 , 41 ]. The synchronously recorded EEG and eye tracking
signals were aligned with the help of sync-triggers. Client-
side Ja v aScript Ajax (asynchronous Jav aScript and XML)
calls sent HTTP requests e v ery 500 ms that in turn called a
function on the backend (Flask web serv er) that elicited the
subsequent recording of EEG and eye tracking time-stamps.
These time-stamps were used to estimate the parameters of
a linear regression function for the mapping of e ye-track er-
time to EEG-time. The EEG data were lo w-pass filtered with
a second order Chebyshe v filter (42 Hz passband, 49 Hz stop
band), do wn-sampled to 100 Hz, re-referenced to the digitally
linked-mastoids and high-pass filtered with a Butterw orth
filter at 0.5 Hz. The last 500 ms of each stimulus presentation
were not considered for the analysis in order to a v oid con-
founds from the terminating b utton press. The first three result
pages were only used for practice and not for analysis.
The proper calibration of the e ye tracker was re-v alidated
at least four times during the experiment and more often if the
subject was unsteady and mo v ed a lot. A picture was consid-
ered as fixated if the location detected by the online algorithm
of the eye track er was situated within the borders of the pic-
ture plus 20 pixels (
0.52 ◦

). The pictures had a side length of
186 pixels and subtended a visual angle of
5.0 ◦

. The size was
picked to fit approximately into the area with high fo v eal reso-
lution. The distance between the pictures was 35 pix els (
0.9 ◦

)
in horizontal direction, 40 pixels (
1.1 ◦

) in vertical direction,
Figure 1. Exemplary stimulus presentation. Left: selecting one cate gory of the underspecified search term ‘ Berlin ’ . Right: the result page
contains pictures from both categories (either ‘ Berlin-Brandenb urg Gate ’ or ‘ Berlin-T elevision T o wer ’ ) and fe w ‘ odd ’ pictures (room, park,
car) that are not related to the search term. The original photographies were replaced by similar o wn pictures in this illustration due to
copyright restrictions.
J. N eur al E ng . 15 ( 2 0 18 ) 026002

J-E Golenia etal
4
181 pixels (
4.8 ◦

) to the horizontal screen borders and 100
pixels (
2.7 ◦

) to the vertical screen borders.
The stimuli were presented with web technologies in
order to explore the compatibility of the BCI-based rele v ance
detector with common software applications, which are not
optimised for the presentation of e xperimental stimuli (fron-
tend: HTML5, CSS, Ja v aScript, jQuery , Ajax, Bootstrap,
backend: Flask). The e xperiment was interacti ve and not a
static prearranged sequence of stimuli. The user could navi-
gate between dif ferent menu pages (e.g. a page for trial selec-
tion) and could calibrate and v alidate the e ye tracker inside
the bro wser under the supervision of the e xperimenter . F or
demonstration purposes, it was additionally possible to train
a classification model with the data recorded so far using a
preliminary version of the classification procedure presented
in section 2.4.1 . This option was gi ven to the participants
after the end of the main recording session. Then, a ‘ feedback
mode ’ could be launched, that allo wed for an online predic-
tion of the respecti v e category of interest (not described fur -
ther in this paper).
2.4. Dat a analy sis
2.4.1 . P rediction of the c ategor y of inter est. Every result page
contained pictures of the two possible interpretations of the
ambiguous search term, which will be referred to as cate go-
ries . In addition, fe w odd pictures were mix ed in, which did
not belong to any of the tw o cate gories. The subjects selected
one category of interest before the display of each result page
and labelled it as tar get cate gory by pressing a button. The
respecti v e other category w as labelled as non-tar get cate gory .
The selected tar get category of e very result page was inferred
from feature vectors e xtracted from the EEG and e ye tracking
signals in two steps (see figure 2 ). First, EEG- and eye-track-
ing-based feature vectors were classified separately (details
are set out belo w). Then, information from EEG and eye
tracking was combined by a v eraging the classifier estimates
of the two measurement modalities. The cate gory with the
lar ger av erage tar get estimate was considered to be the tar get
category of the respecti ve result page. Binary classifications
were performed, because the odd images were not considered.
Linear discriminant analysis (LD A) with shrinkage served as
classifier , which regularises the estimated co v ariance matrix
and, thereby , reduces the likelihood of o verfitting in the case
of high-dimensional data and a limited number of samples
[ 42 , 43 ]. The optimal shrinkage parameter w as calculated ana-
lytically using the closed form equation deri ved in [ 44 ], which
is computationally less expensi ve than choosing the optimal
parameter by cross-v alidation. Posterior probabilities were
computed from the classifier scores because probabilities are
well suited for combining dif ferent classifier estimates due to
the clear upper and lo wer limit and the same scale. The pre-
dicti v e performance was assessed in ten-fold cross-v alidations
using the classification accuracy as metric.
For the EEG-based prediction, feature v ectors corre-
sponding to each fixated image were classified as being either
members of the tar get or the non-tar get category . The tar get
probabilities of all feature vectors per cate gory were a v er -
aged. The category with the lar ger a v erage target probability
can be assumed to be the selected category of interest of the
respecti v e result page. Feature vectors were e xtracted from
the continuous multi-channel EEG signals as follo ws. One
second long epochs aligned to the onsets of the longest eye
fixations of each image were cut out (fixation-related poten-
tials; ‘ FRPs ’ ) and do wnsampled to 20 Hz (which reduced the
dimensionality of the feature vectors and thereby the risk of
ov erfitting to the training data). The data of all 62 channels
were concatenated in one feature vector with 1240 dimen-
sions. The number of samples (longest fixations on either
tar get or non-target images) ranged from 2821 to 5165 per
Figure 2. Prediction of the category of interest (flo w chart of the data analysis described in section 2.4.1 ).
J. N eur al E ng . 15 ( 2 0 18 ) 026002

J-E Golenia etal
5
single subject, with slightly unbalanced classes, because tar get
images were fixated more often than non-tar get images. Note,
that only fixated images could contrib ute to the inference.
For a performance comparison, the first and the last fixation
were also tested as time markers of reference — in addition to
the default usage of the longest fixation. Methods for artef act
rejection were not applied in order to let the classifier learn
to deal with potential artefacts in the signals. From e xperi-
ence, this approach is superior to artefact rejection/correc-
tion in laboratory experiments with artef acts that are not too
se v ere. A rob ust classifier can deal with artefacts during online
operation, while artefact rejection w ould lead to missing data,
which is critical in many online applications.
For the e ye-tracking-based prediction, feature v ectors
were extracted separately per cate gory and result page, and
were classified with shrinkage LD A. These scr een -based
eye tracking features comprised the mean dwell time, the
median and maximum fixation duration and the a verage fixa-
tion number . The cate gory with the larger tar get probability
was considered to be the selected cate gory of interest of the
respecti v e result page. In addition, an alternati ve classifica-
tion strategy w as e xamined, which resembled the procedure
of the EEG-based prediction: each image was first classified
as member of the tar get or non-target cate gory based on the
dwell time on each image ( single -image eye tracking fea-
tures). Then, the single probabilities were av eraged per cat-
egory ( a ggr e gated eye tracking probabilities). Shrinkage w as
not necessary in this case because cov ariances can not be con-
sidered for this uni v ariate feature.
In addition, feature vectors e xtracted from the EOG were
classified in order to assess a possible contrib ution of eye
mov ements to the EEG-based prediction (horizontal e ye
mov ements were captured by subtracting channel F10 from
channel F9 and vertical e ye mo vements by subtracting channel
Fp1 from the signal of the electrode belo w the e ye).
2.4.2. Charact eristics of the EEG and e ye tracking f eatures.
The characteristics of the EEG epochs, which served as fea-
tures for the classifications, were assessed separately for the
three groups of the corresponding images (tar gets, non-tar -
gets, odds). Discriminati v e information between target v er -
sus non-tar get EEG epochs, between target v ersus odd EEG
epochs, and between non-tar get versus odd EEG epochs w as
inspected for each time point and each EEG channel with the
point biserial correlation coef ficient, which was squared while
retaining the sign ( r 2 ). The eye mo v ements were character-
ised with fixation maps of the result pages, and by computing
the statistics of the dwell time, of the number of fixations and
of the median and maximum fixation duration of tar get, non-
tar get and odd images.
2.4.3. T ask perfor mance. The beha vioural performance and
compliance of each participant with the task instructions was
assessed by computing the percentage of correct answers, the
de viation of the number entered by the subject from the true
number of images belonging to the selected category , and the
trial durations.
3. Results
3.1 . P rediction of the c ategor y of inter est
The chosen category of interest of the ambiguous search
term could be inferred with an accuracy of 85.9% ± 5.8%,
when information from EEG and eye tracking w as combined
(mean ± standard de viation; the results of the single subjects
ranged from 73% to 95%; see figure 3 ). This outcome is signifi-
cantly better than the chance le v el of 50% that can be expected
from random guessing (
p < 0.05

, W ilcoxon signed rank test on
the population le v el). When only EEG features were used, the
estimates were correct in 76.9% ± 8.7% (
p < 0.05

; ranging
from 56.0% to 90.1%), and in 81.0% ± 6.7% for predic-
tions with screen-based e ye tracking features only (
p < 0.05

;
ranging from 67.6% to 92.8%). The complementarity of infor -
mation provided by the single modalities w as e v aluated sepa-
rately for EEG and eye tracking. A subset of the samples was
selected where the prediction based on the respecti v e alter-
nati v e modality was wrong (i.e. the full set of samples was
reduced by about 81.0% and 76.9% respecti v ely). The predic-
ti v e performance on the subset decreased merely for about five
percentage points in comparison to the full set, and was still
significantly better than random (EEG if eye tracking wrong:
71.5%, eye tracking if EEG wrong: 76.8%), which indicates
complementarity (W ilcoxon signed rank tests,
p < = 0.05

).
EOG features resulted in a predicti v e performance closer to
the chance le v el of 50% in comparison to the other modalities
(see figure 3 ).
The predicti v e performance based on EEG features only is
sho wn in figure 4 . The cate gory of interest was estimated by
aggreg ating the category membership probability estimates of
the single images (see black and gre y boxplots in figure 4 ).
Figure 3. Identifying the selected target cate gory of the ambiguous
search term with information extracted from the dif ferent signals
( ‘ ET ’ stands for eye tracking). The classification accurac y served as
metric for the predicti ve performance. The chance le v el of a random
classifier would be situated at 50%. Ev ery boxplot represents the
a verage cross-v alidation results of the participants of the study . Red
lines indicate the median v alues, blue diamonds the mean, black
boxes the 25th and 75th percentiles, whisk ers the range, and crosses
the outliers. EEG and EOG epochs used for the classifications were
aligned to the longest fixation.
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J-E Golenia etal
6
The class-wise normalised accuracy , which is insensitiv e to
class imbalances, served as performance metric in the case
of the single image classification, because tar get images were
fixated more often than non-tar get images. Using the longest
fixation as time marker of reference (for the feature e xtraction
from the continuously recorded EEG) resulted in a slightly
better accuracy in comparison to the usage of the first or the
last fixation on an image.
The category of interest could be predicted better than
random with screen-based eye tracking features, b ut not
with single-image eye tracking features (also when the
resulting probabilities were aggreg ated per category; see
figure 5 ).
3.2. Char acteristics of the EEG and e ye tr acking f eatur es
Characteristic neural responses were elicited when either
tar get, non-target or odd images were fixated. An EEG comp-
onent occurred at about 500 ms to 700 ms after the onset of
the longest fixation, and allo wed for discriminating tar gets
from non-tar gets and odds (see figures 6 and 7 ). Dif ferences
between the corresponding EEG epochs were most prominent
at central and parietal electrodes. For conciseness, we only
display the results of the longest fixation, because the spatial
distrib utions and time courses of the dif ferent fixations were
very similar (with a small time lag).
Result pages were scanned in a systematic order , starting in
the upper left corner (at the position of the fixation cross) and
then continuing ro w by ro w to the bottom right (see figure 8
for a typical fixation map). Fe w subjects e xamined column by
column, almost all subjects applied the same search strategy
to most of the search screens.
Dif ferent fixation patterns were observ ed for target, non-
tar get and odd pictures (see figure 9 ). Dwell time, number of
fixations and median and maximum fixation duration were
significantly lar ger for targets than for non-tar gets (
p < 0.05

,
W ilcoxon signed rank test across all subjects; medians: 523 ms
versus 339 ms, 2.15 versus 1.6, 218 ms v ersus 196 ms, 551 ms
versus 428 ms) and distributed more broadly as indicated by
the standard de viations (dwell time: 248 ms v ersus 210 ms,
number of fixations: 0.82 v ersus 0.73, fixation duration: 44 ms
versus 40 ms, maximum fixation duration: 221 ms versus
207 ms). The odd distrib utions hav e non-empty bins at zero,
because sometimes all odd images of a result page were
skipped.
3.3. T ask perfor mance
Correct answers were gi v en in 45.7 ± 14.2% of the cases
(mean ± standard de viation), ranging from 20% to 63%.
Participants tended to miss a tar get rather than counting too
many (see figure 10 , bottom). The participants spent a median
time of about 15 s and rarely more than 20 s on each result
page with 24 images. Accordingly , single images were typi-
cally vie wed less than one second.
4. Discussion
4.1 . P rediction of the c ategor y of inter est
Ambiguity in image search was resolv ed by inferring
the intended meaning of the underspecified query term
from information present in EEG and/or eye tracking sig -
nals. Predicting the category of interest was possible with
both measurement modalities. Combining the modalities
improv ed the predicti ve performance, which suggests that
EEG and eye tracking pro vide complementary information
(see section 3.1 and figure 3 ). The follo wing findings gi ve
further e vidence for this claim: testing only samples that
were misclassified by the respecti v e other modality resulted
in an accuracy that w as still significantly better than random
(see section 3.1 ). Thus, the classifiers made dif ferent mis -
takes and e xploited dif ferent information. Moreo ver , dis -
criminati v e information present in the fixation-related EEG
Figure 4. Predicti ve performance with EEG features only . The
category of interest w as predicted ( blac k ) by aggregating the
category membership estimates of the single images ( g rey ; class-
wise weighted accuracies). Either the first, the longest, or the last
fixation on an image served as time mark er of reference for the
feature extraction from the continuous EEG.
Figure 5. Predicti ve performance using either the single -image e ye
tracking features, the ag gr e gated eye tracking probabilities, or the
scr een -based eye tracking features. The chance le vel of a random
classifier would be situated at 50%.
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J-E Golenia etal
7
epochs was found mainly at central electrodes, which are
presumably less confounded by eye mo v ements than elec -
trodes at outer positions (see figure 7 ; e ye mov ements may
ha ve influenced the EEG responses of the single classes; see
the topographies in figure 6 ). Besides, dif ferences in the EEG
started at about 500 ms after fixation onset (see figure 7 ),
and, therefore, mainly after the onset of the follo wing e ye
mov ement (see figure 9 ).
Accumulating e vidence (classifier probabilities) o ver se v-
eral feature vectors considerably impro ved the EEG-based
predicti v e performance (see section 3.1 and 4 ). Thus, the find-
ings demonstrate that the inherent uncertainty of the single
rele v ance estimates (here: for single images) can be o vercome
by including information about the membership to a more
general category (here: possible interpretations of an ambig-
uous term). This insight can be taken into account also by
Figure 6. A v erage EEG responses to the longest fixation of target, non-tar get and odd pictures ( left: time courses at electrode Cz; right:
scalp maps with all electrodes in two selected temporal interv als; averages o v er all EEG epochs of all participants).
Figure 7. Statistical dif ferences (signed r 2 v alues) between target v ersus non-tar get EEG epochs (top), between tar get versus odd EEG
epochs (centre), and between non-target v ersus odd EEG epochs (bottom). The epochs were aligned to the longest fixations of the images.
The channels are ordered from the front to the back and from the left to the right side of the head. A verages o ver all subjects of the study are
sho wn for all time points ( left ) and for two selected interv als as scalp maps ( right ). A significance threshold was not applied in order to keep
also subtle dif ferences that can potentially be exploited by the multi variate classifier (see section 2.4.1 ).
J. N eur al E ng . 15 ( 2 0 18 ) 026002

J-E Golenia etal
8
future ef forts that apply brain-computer interfacing to human-
computer interaction.
The ef fect of the number of test samples used for e vidence
accumulation on the certitude of the final prediction is inspected
in more detail in [ 21 ]. In addition, the expectable generalization
performance of a predicti v e model typically gro ws with more
training samples a vailable, b ut has to be weighed up against the
ef fort and the duration to acquire more training samples. This
trade-of f depends on the specifics of the future application. W e
therefore decided not to in vestigate this dependenc y in more detail
for the current study , which in vestigates merely a demonstrator .
The longest fixations may ha ve resulted in the best EEG-
based predicti v e performance (see section 3.1 and figure 4 )
because they presumably serv ed for a closer inspection of
informati v e spots of the picture (and were not only interme-
diate stops on negligible spots).
Figure 9. Distrib utions of the four eye tracking features, a v eraged ov er all subjects, for the three categories ‘ tar gets ’ (green), ‘ non-tar gets ’
(red) and ‘ odds ’ (grey).
Figure 8. Exemplary fixation map of a participant inspecting a result page. The participant searched pictures of one cate gory of the
ambiguous search term that are represented here by white tiles (due to copyright restrictions) and w as less interested in pictures of the
second category (black tiles). Three ‘ odd ’ pictures (gre y tiles) were not related to the search term. Eye fixations are indicated by blobs with
surfaces proportional to the respecti ve fixation duration. The first, the last and e very fi fth fixation are labelled. Colours indicate the order of
the fixations (from blue to red).
J. N eur al E ng . 15 ( 2 0 18 ) 026002

J-E Golenia etal
9
4.2. Char acteristics of the EEG and e ye tr acking f eatur es
The fixation of non-tar get and odd images e v oked a late posi-
ti v e complex, in contrast to tar get images (see section 3.2
and figures 6 and 7 ). The ef fect occurred later than it can be
expected from the EEG component ‘ P300 ’ , which is e vok ed
by the oddball paradigm [ 45 ]. The stimuli were photographies
that dif fered not only in lo w-le v el features, which could be
quickly recognised (e.g. texture, contrast, colour), b ut also in
high-le v el features, which had to be interpreted (e.g. scene
or object depicted). Note that the e xperimental design does
not exactly match the classic oddball paradigm, because the
probabilities of tar get and non-target stimuli were equal. Non -
tar get and odd images did not fit the expectations of the par -
ticipant, stood out in the ‘ regular train of standard stimuli ’
[ 45 ], and might be compared to the so called tar get stimuli of
the classic oddball paradigm. For this reason, the late positi ve
complex may appear to be in verted at the first glance (see an
alternati v e explanation belo w).
Images were often fixated only once (see section 3.2 ).
Thus, the longest fixation was in man y cases the first and the
last fixation at the same time. The distributions of the e ye
tracking features corresponding to the three image catego-
ries (tar get, non-target, odd) o v erlap, b ut are clearly not the
same (see figure 9 ). T arget images were, in general, fixated
longer and more frequently than non-tar get images. Thus, an
image was more lik ely follo wed by a tar get image than by a
non-tar get (or odd) image, e ven though the presentation prob-
ability was the same for tar get and non-tar get images (see sec-
tion 2.1 ). Imbalanced dwell times and transition probabilities
may ha ve systematically distorted the e vent-related potentials
at later time points, when the next image w as already fixated,
and could ha ve resulted in the found late positi ve comple x.
4.3. T ask perfor mance
The participants complied with the task instructions, because
the images were skimmed quickly and not inspected thor -
oughly , as suggested by the comparably short time spent on
each result page and the rather lo w counting accurac y (see
section 3.3 and figure 10 ).
5. Conclusion
The study sho ws that EEG and e ye tracking signals can be
used to infer the subjecti v e rele v ance of screen content. This
implicit information can be extracted from the signals in the
background and makes it possible to create dynamic user
interest profiles in real-time without an explicit rele v ance
feedback from the user . A whole ne w range of applications
can be concei v ed on the basis of the introduced technolo-
gies, e v en though the purpose of use presented in this paper
is rather specific (ambiguities in image search were resolved).
Computer users could na vigate rapidly through lar ge data
sets with little ef fort using no vel interf aces tailored to the
implicit rele v ance feedback from the sensors. Eye tracking
is especially promising considering the progress made with
reg ard to technology and cost [ 23 ]. Ne vertheless, recently
de v eloped miniaturised EEG systems with dry electrodes can
be set-up quickly and hassle-free (see section 1 ), and a small
set of electrodes may be suf ficient, because central areas of
the scalp were particularly informati v e (see section 3.2 and
figures 6 and 7 ). While both measurement modalities turned
out to be complementary (see sections 3.1 and 4.1 ), informa-
tion provided by e ye tracking might v anish in a more realistic
setting (b ut is ne vertheless required for the feature extraction
from the EEG). Discriminati v e information present in fixa-
tion duration and dwell time could be corrupted when the user
starts pondering and interrupts the flo w of the e ye mov ements.
In contrast, spatio-temporal patterns in short fixation-related
EEG epochs may remain unaf fected. Besides, EEG contained
information about the rele v ance of the single images, which
could be used for more fine-grained user interest profiles (see
figure 4 ), in contrast to eye tracking, which allo wed only for
estimating the rele v ance of the entire page (see figure 5 ).
A c knowledgments
The research leading to these results has recei v ed funding
from the European Union Se v enth Frame work Programme
(FP7/2007-2013) under grant agreement
n ◦

611570. The
work of Benjamin Blank ertz was additionally funded by the
Bundesministerium f ü r Bildung und Forschung under con-
tract 01GQ0850.
ORCID iDs
Markus A W enzel https://orcid.or g/0000-0002-6540-1476
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