Combining EEG and eye tracking: Using fixation-locked potentials in visual search

Abstract

Visual search is a complex task that involves many neural pathways to identify relevant areas of interest within a scene. Humans remain a critical component in visual search tasks, as they can effectively perceive anomalies within complex scenes. However, this task can be challenging, particularly under time pressure. In order to improve visual search training and performance, an objective, process-based measure is needed. Eye tracking technology can be used to drive real-time parsing of EEG recordings, providing an indication of the analysis process. In the current study, eye fixations were used to generate ERPs during a visual search task. Clear differences in ERPs were observed following training, suggesting that neurophysiological signatures could be developed to prevent errors in visual search tasks.

Full text

ORIGINAL RESEARCH ARTICLE
published: 09 October 2012
doi: 10.3389/fnhum.2012.00278
Combining EEG and eye tracking: identification,
characterization, and correction of eye movement
artifacts in electroencephalographic data
Michael Plöchl1*, José P. Ossandón1and Peter König1,2
1Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany
2Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
Edited by:
John J. Foxe, Albert Einstein College
of Medicine, USA
Reviewed by:
Olaf Dimigen, Humboldt Universität
zu Berlin, Germany
Daniel Belyusar, Albert Einstein
College of Medicine, USA
Shlomit Greenberg, New York
University, USA
*Correspondence:
Michael Plöchl, Institute of Cognitive
Science, University of Osnabrück,
Albrechtstraße 28, 49069
Osnabrück, Germany.
e-mail: mploechl@uni-osnabrueck.de
Eye movements introduce large artifacts to electroencephalographic recordings (EEG)
and thus render data analysis difficult or even impossible. Trials contaminated by eye
movement and blink artifacts have to be discarded, hence in standard EEG-paradigms
subjects are required to fixate on the screen. To overcome this restriction, several
correction methods including regression and blind source separation have been proposed.
Yet, there is no automated standard procedure established. By simultaneously recording
eye movements and 64-channel-EEG during a guided eye movement paradigm, we
investigate and review the properties of eye movement artifacts, including corneo-retinal
dipole changes, saccadic spike potentials and eyelid artifacts, and study their interrelations
during different types of eye- and eyelid movements. In concordance with earlier studies
our results confirm that these artifacts arise from different independent sources and
that depending on electrode site, gaze direction, and choice of reference these sources
contribute differently to the measured signal. We assess the respective implications for
artifact correction methods and therefore compare the performance of two prominent
approaches, namely linear regression and independent component analysis (ICA). We
show and discuss that due to the independence of eye artifact sources, regression-based
correction methods inevitably over- or under-correct individual artifact components, while
ICA is in principle suited to address such mixtures of different types of artifacts. Finally,
we propose an algorithm, which uses eye tracker information to objectively identify
eye-artifact related ICA-components (ICs) in an automated manner. In the data presented
here, the algorithm performed very similar to human experts when those were given
both, the topographies of the ICs and their respective activations in a large amount of
trials. Moreover it performed more reliable and almost twice as effective than human
experts when those had to base their decision on IC topographies only. Furthermore, a
receiver operating characteristic (ROC) analysis demonstrated an optimal balance of false
positive and false negative at an area under curve (AUC) of more than 0.99. Removing the
automatically detected ICs from the data resulted in removal or substantial suppression of
ocular artifacts including microsaccadic spike potentials, while the relevant neural signal
remained unaffected. In conclusion the present work aims at a better understanding
of individual eye movement artifacts, their interrelations and the respective implications
for eye artifact correction. Additionally, the proposed ICA-procedure provides a tool for
optimized detection and correction of eye movement-related artifact components.
Keywords: eye tracking, EEG, independent component analysis (ICA), regression, artifact correction, eye
movements
INTRODUCTION
Neural activity measured with electroencephalography (EEG) or
magnetoencephalography (MEG) yields a relatively weak signal,
with amplitudes typically in the order of a few microvolts or fem-
totesla, respectively. At the same time, such measurements at scalp
level are prone to electrical artifacts originating from non-neural
sources such as the eyes, muscles or electrical devices in the sur-
roundings. Compared to signals resulting from neural processes
these artifacts can be several magnitudes larger in amplitude.
Thus, cerebral activity may be buried in noise and remain unde-
tectable even when a large number of trials is averaged.
A common strategy to circumvent this problem is to discard
artifactual epochs from the data. This however often leads to
considerable data loss, which in turn reduces the signal-to-noise
ratio improvement that results from averaging techniques and
therefore also the ability to detect neural activity. Additionally,
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HUMAN NEUROSCIENCE

Plöchl et al. Characterization and correction of eye artifacts
this approach entails methodological constraints: in standard
EEG or MEG experiments subjects are required to not move
their eyes, thus adding unnatural cognitive loads to the exper-
imental task and precluding the realization of EEG and MEG
studies under more natural viewing conditions. Furthermore,
the direct study of overt attention dynamics, by exploiting the
temporal resolution of non-invasive electrophysiological meth-
ods, is rarely pursued. In studies that attempt to do so, the
common practice is to analyze only the epochs before or after
the eye movement and in this way to avoid problems aris-
ing from eye movement artifacts. However, some artifacts can
extend to periods before and after saccade on- and offset,
respectively, and thus the analysis of those periods may be con-
founded (Thickbroom and Mastaglia, 1985; Becker and Fuchs,
1988).
Likewise, studies that are not directly related to eye move-
ments are also potentially impaired: when undetected, even
small but systematic artifacts can add up over trials, thus
distorting the analysis and the conclusions drawn from it
(Hillyard and Galambos, 1970). In this context small invol-
untary eye movements during fixation, usually referred to as
miniature eye movements (Rucci et al., 2007)ormicrosac-
cades (Martinez-Conde et al., 2004; Engbert, 2006), have recently
received considerable attention, as they have been shown to vary
systematically between different cognitive or perceptual states
and in this way to introduce systematic biases in the analysis
of neural activity (Yuval-Greenberg et al., 2008; Keren et al.,
2010).
In order to overcome such limitations and biases, several
procedures have been proposed in order to remove or at least
reduce artifacts present in the data. These procedures include
averaging, filtering (Luck, 2005), linear regression (Elbert et al.,
1985; Croft and Barry, 2000a,b; Schlögl et al., 2007), Principal
Component Analysis (Lins et al., 1993a; Kierkels et al., 2007),
independent component analysis (ICA) (Jung et al., 2000; Iriarte
et al., 2003; Joyce et al., 2004; Li et al., 2006; Mateenuddin
et al., 2008), dipole modeling (Berg and Scherg, 1991; Lins
et al., 1993a) and frequency methods (Croft and Barry, 2000a,b).
Most of these approaches have been shown to effectively remove
artifacts whose respective sources are well defined and whose
spectral and statistical signal properties (e.g., amplitude, vari-
ance, frequency range, kurtosis etc.) differ considerably from
those of neural activity. Averaging procedures for example sub-
stantially reduce random noise of moderate amplitude and
therefore are standard in most electrophysiological experiments
(Luck, 2005). Furthermore slow signal drifts caused by chang-
ing electrode properties, line noise and high frequency mus-
cle activity can relatively easily be removed from the data by
applying appropriate filters. Other muscle artifacts are reli-
ably isolated into ICA components that can then be excluded
from the data (Makeig et al., 1996; Jung et al., 2000; Iriarte
et al., 2003). For eye movements on the other hand there
is no standard correction procedure established yet, although
several proposals do exist and are extensively discussed with
respect to their efficiency (Croft and Barry, 2000a,b; Croft
et al., 2005; Schlögl et al., 2007; Hoffmann and Falkenstein,
2008).
The difficulty to identify and correct eye movements can be
largely attributed to the fact that a single eye movement pro-
duces several artifacts in the form of signal offsets and transients.
These artifacts do not only emerge from different mechanisms but
also differ in their statistical and spectral properties, depending
on size and direction of the movement: while artifacts pro-
duced by eyeball rotation are the consequence of a direction
change of the corneo-retinal dipole and therefore change roughly
linearly with movement size and direction, blink artifacts are gen-
erated by the cornea being short-circuited to the extra ocular
skin, thus being independent of corneo-retinal dipole orienta-
tion (Matsuo et al., 1975; Antervo et al., 1985; Chioran and
Yee, 1991). In addition, the amplitude of the (pre-)saccadic spike
potential, an artifact that most likely emerges from extra-ocular
muscle activity, changes only marginally with saccade amplitude
or direction (Keren et al., 2010; Carl et al., 2011). Therefore it
may confound the data even during very small eye movements
(i.e., microsaccades) that do not produce clearly visible corneo-
retinal dipole offsets (Yuval-Greenberg et al., 2008). Another
difficulty for the general characterization of eye movement arti-
facts is that there are also non-physiological factors that may
alter the signal when eye movements are present in the data.
As a result of high pass filtering during EEG recording, step-
like signal changes, as they occur during eyeball rotation, cause
the signal to slowly drift back toward its initial value accord-
ing to the filter’s cut-off frequency. Such drifts may confound
the data up to several seconds after the actual saccade. Finally,
re-referencing the data (e.g., to the average activity of all scalp
electrodes) can change the sign and amplitude of a given arti-
fact component at different recording sites, thus rendering its
identification difficult.
In the literature different types of eye artifacts are usually
reviewed individually (Thickbroom and Mastaglia, 1985; Chioran
and Yee, 1991; Lins et al., 1993a,b; Keren et al., 2010). To our
knowledge a comprehensive overview that also accounts for their
interrelations during different saccade types does not exist.
Moreover, the heterogeneity of sources contributing to signal
contamination by eye movements has important implications for
correction procedures. Algorithms that assume that eye move-
ment artifacts originate from a single source—or ignore that the
relative contribution of different artifact sources to the signal may
vary depending on the respective recording site and movement
direction—will generally over- or under-correct the signal even if
being accurate at one particular site. On the other hand, algo-
rithms that can account for multiple and independent artifact
sources (e.g., blind source separation methods) often depend on
subjective decisions, as for example which of the isolated compo-
nents relate to eye movements and therefore should be excluded
from the data (Makeig et al., 1996;Jung et al., 2000).
In the following, using EEG and eye tracking during a guided
eye movement paradigm, we will first review different types of
artifacts produced by eye movements and investigate their pro-
jections to different electrode sites during a variety of saccades.
Subsequently we will point out the advantages and disadvantages
of different approaches to eye artifact correction and then pro-
pose a cleaning procedure to remove eye artifacts in an effective
and objective manner.
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Plöchl et al. Characterization and correction of eye artifacts
METHODS
PARTICIPANTS
We simultaneously recorded EEG and eye movements from 14
subjects (7 male, 7 female; age range: 20–31 years) after they were
informed about the procedure and purpose of the study and had
signed an informed consent. Experimental procedures conformed
to the Declaration of Helsinski and national guidelines. All sub-
jects were students at the University of Osnabrück and received
payment or course credits for their participation. All subjects
reported normal or corrected-to-normal vision.
STIMULUS PRESENTATION
The participants sat at 60 cm distance from a 30 TFT moni-
tor (Apple LED Cinema Display, refresh rate 60 Hz, resolution
2560 ×1600 pixels). Due to the range of the eye tracker only a
square region of 960 ×960 pixels (∼23◦×23◦)inthecenterof
the screen was used for stimulus presentation. The stimulus itself
constantly covered about 3◦of the visual field and consisted of
black and white rings (see Figure 3), which continuously con-
tracted toward the center at a rate of 2 Hz (i.e., when the outer
ring started contracting into the center it was replaced by the next
ring of the other color).
EXPERIMENTAL PROCEDURE
Previous to each block of the actual experiment, subjects per-
formed a short pre-experimental procedure in order to cal-
culate the regression coefficients (Schlögl et al., 2007)andto
complement the data for ICA decomposition (see below). This
pre-experiment consisted of 16 trials of 15 s duration in which
subjects were asked to perform different eye movements over
a gray screen (RGB 127/127/127). Every subject performed
four trials for each of the following movements: blinks, verti-
cal movements, horizontal movements, and blinks plus vertical
movements. On average each subject performed 169 blinks, 561
vertical, and 312 horizontal saccades during the pre-experiment.
After the pre-experiment the participants performed the
task illustrated in Figure 1: a white fixation cross (size ∼1.5◦)
appeared on a gray screen (RGB 127/127/127) in one of nine pos-
sible locations arranged in a three by three square. The respective
trial started as soon as the subject started fixating the fixation
cross. After a variable time (500–800 ms) the stimulus was pre-
sented in one of the other eight remaining positions on the screen
(Figure 1A).
In one condition (saccade condition) the fixation cross disap-
peared after another 500–1000 ms, thus providing the cue for the
subject to make a saccade to the stimulus location (Figure 1B).
In the other condition (fixation condition), instead of the fixa-
tion cross disappearing, the stimulus disappeared in its original
location and replaced the fixation cross on the fixated posi-
tion (Figure 1C). Thus, at each location stimulus foveation could
result from either an eye movement or the sudden appearance of
the stimulus. The stimulus was presented for 1200 ms after disap-
pearance of the fixation cross (saccade condition) or relocation of
the stimulus (fixation condition), respectively. Each experimen-
tal block consisted of 480 saccade trials and 480 fixation trials,
which were randomly interleaved. All subjects performed at least
two pre-experimental and two experimental blocks.
FIGURE 1 | Experimental task. (A) Depending on the stimulus location
relative to the fixation cross, subjects performed horizontal and vertical
(white arrows) or oblique (red arrows) saccades on the screen. Left: short
saccades (11.5◦vertical and horizontal, 16.2◦oblique) were either
performed from the periphery to the center or from the center to the
periphery. Right: long saccades (23◦vertical and horizontal, 32.5◦oblique)
were performed from the periphery to another peripheral point located
opposite across the center. (B) Each trial started as soon as the subject
began to fixate the fixation cross located in one of nine possible locations
on the screen. After a variable time of 500–800 ms the stimulus was shown
in one of the other eight remaining locations on the screen. In the saccade
condition the fixation cross disappeared after another 500–1000 ms, thus
serving as the cue for the subject to make a saccade onto the stimulus. (C)
In the fixation condition, instead of the fixation cross disappearing, the
stimulus was relocated onto the position where the subject fixated.
EYE TRACKING
Eye movements were recorded with a remote video eye track-
ing system using monocular pupil tracking at 500 Hz (Eyelink
1000, SR Research Ltd., Mississauga, Canada). To calibrate eye
position a 13-point grid was used and the calibration procedure
was repeated until the average error was below 0.5◦.Saccades
were defined using a velocity threshold of 30◦/s, an accelera-
tion threshold of 8000◦/s2, and a minimum deflection threshold
of 0.1◦.
MICROSACCADE DETECTION
Using an algorithm published by Engbert and Mergenthaler
(Engbert and Mergenthaler, 2006), microsaccades were defined
as intervals in which the recorded eye movements exceeded a rel-
ative velocity threshold of six median-based standard deviations
for a duration of at least six samples (12 ms) and had an amplitude
between 0.1◦and 1◦. Microsaccade detection was only performed
on fixation trials that did not contain any saccades larger than 1◦
(according to the criteria described in “Eye Tracking”).
EEG RECORDINGS
EEG-data were recorded using an ActiCap 64-channel active elec-
trode system with a BrainAmp DC amplifier (Brain Products
GmbH, Gilching, Germany). 61 of the electrodes were placed
equidistantly on the scalp, one was placed on the forehead
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Plöchl et al. Characterization and correction of eye artifacts
(approximately 25 mm above the nasion), and another two on
the left and right infraorbital rim, respectively. The impedance of
all electrodes was reduced below 5 k. The data were recorded
at a sampling rate of 1000 Hz and online band-pass filtered
between 0.016 Hz and 250 Hz. During recording all electrodes
were referenced to a nose tip electrode.
PREPROCESSING AND ANALYSIS
All data were preprocessed and analyzed in Matlab (Mathworks)
and using FieldTrip (Oostenveld et al., 2011) and EEGLAB
(Delorme and Makeig, 2004).
EEG data were down-sampled to 500 Hz in order to match
thesamplingrateoftheeyedataandlow-passfilteredat100Hz.
Eye- and EEG data were then aligned by cutting them into tri-
als according to the triggers that were simultaneously sent to
both, the EEG and the eye tracking system. We visually inspected
the EEG data and removed trials containing high amplitude
noise, as it typically arises from muscle activity related to larger
body movements, as well as blinks and other easily identifiable
confounds such as sudden electrode drifts and jumps. Trials con-
taining saccade-related artifacts were not excluded from analysis
unless (1) they were not related to the task and exceeded 2◦of
visual angle in the saccade condition or 1◦during fixation trials,
(2) there was no stable fixation within 400 ms after the cue, or (3)
they were not located in a window of 2◦around the center of the
stimulus.
Where explicitly stated in the results section, the EEG data were
re-referenced to the average activity of the 61 scalp electrodes.
EVENT-RELATED POTENTIALS
For the analysis of event-related potentials (ERPs) the trials were
cutinto1slongepochsrangingfrom−500 to +500 ms around
the event of interest (i.e., saccade on- or offset in the saccade
condition and stimulus relocation in the fixation condition).
Subsequently the trials were baseline corrected to the pre-event
interval and then averaged for each condition over all subjects.
Deviations from this procedure will be detailed in the respective
result sections.
TIME-FREQUENCY ANALYSIS
Time-frequency analysis of the gamma frequency band (>30 Hz)
was performed using a set of complex Morlet wavelets with a
width of five cycles per frequency. The spectral estimate was
obtained for frequencies ranging from 30 to 100 Hz in steps of
2Hz.
For the analysis of the frequency bands below 30 Hz we used
Fast Fourier Transform instead of wavelet analysis in order to
obtain a constant frequency resolution. More specifically we seg-
mented each trial into 200 ms long overlapping data windows,
advancing in 5ms steps from −500 to +500 ms. We then mul-
tiplied each data segment with a Hanning window before the Fast
Fourier Transform was computed.
Finally, in order to obtain power changes with respect to
baseline, all time-frequency bins were normalized to the average
pre-stimulus power in each frequency bin and then averaged over
trials and subjects.
INDEPENDENT COMPONENT ANALYSIS
We performed ICA to identify and extract ocular artifact com-
ponents from the data. ICA is a blind source decomposition
algorithm that enables the separation of statistically independent
sources from multichannel data. It has been proposed as an effec-
tive method for separating ocular movement and blink artifacts
from EEG data (Jung et al., 2000; Iriarte et al., 2003; Hoffmann
and Falkenstein, 2008). ICA was performed using the Infomax
ICA algorithm (Bell and Sejnowski, 1995) as implemented in
the EEGLAB toolbox (Delorme and Makeig, 2004). In order to
optimize the ICA decomposition with respect to eye movement-
and blink-related components, we appended the experimental
data with data from a pre-experimental procedure during which
each participant performed blinks and vertical and horizontal
saccades within the same region of the gray screen that would
later be used for stimulus presentation (see experimental proce-
dure). Then, for each subject individually, we decomposed the
preprocessed data from all 64 channels into 64 statistically inde-
pendent components (ICs). To differentiate ICs that are related
to eye movements and blinks from the ones produced by neu-
ral activity and other sources, we followed the procedure that is
illustrated in Figure 2B: first we partitioned every trial into sac-
cade and fixation epochs. Saccade epochs were defined as the
time between saccade on- (Figure 2B, green dotted lines) and off-
set (Figure 2B, red dotted lines) as given by the eye tracker. To
ensure that both, spike potentials and post-saccadic eyelid arti-
facts were comprised by saccade epochs as well, we additionally
included the intervals 5 ms before and 10 ms after saccade on- and
offset, respectively. Conversely, fixation intervals were defined as
the time between saccade epochs. Subsequently, for each trial, we
computed the variance of the respective IC activations during sac-
cades and during fixations (note that in Figure 2 for illustration
purposes we show IC ERPs instead of IC activations during sin-
gle trials). If for a given IC the mean variance of saccade epochs
was at least 10% higher than the mean variance of fixation epochs
(i.e., variancesaccade/variancefixation >1.1), the IC was classified as
eye-artifact-related and subsequently removed from the data. The
threshold of 10% was introduced to avoid that components with
constant variance over both, saccade and fixation epochs, might
be misclassified due to random fluctuations.
Note that here we only rejected eye-artifact-related ICs, while
ICs related to non-ocular artifacts (e.g., muscle activity) were not
excluded from the data.
REGRESSION
We used two different linear models for regression-based artifact
correction. In both models electrooculogram (EOG) channels
were used as the independent variables that convey the signal
of a single artifact source for both eyes. As the movement of
the eyeball with respect to the head can be explained with only
two spatial components, the first model only takes vertical and
horizontal EOG measurements into account:
EEG(t)observed =EEG(t)source +β1vEOG(t)+β2hEOG(t).
It has been proposed that artifacts related to eyelid movement,
which are independent of eyeball movement could be modeled as
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Plöchl et al. Characterization and correction of eye artifacts
FIGURE 2 | Component examples and correction procedure.
(A) Experimental setup. (B) Examples of five typical IC topographies and
their activations, as we found them similarly for all of our subjects. The
first three ICs can be classified as eye movement-related as their
activations display high variance during saccade intervals (between green
and red dotted line), while being inactive during fixation periods (left and
right of green and red dotted line). IC 4 on the other hand displays its
largest variance during fixation. Therefore, it cannot be attributed to
artifacts produced during saccade execution, but instead to neural
activity—in this case the time locked visual response to the stimulus.
Finally, the topography and signal properties of IC 5 suggest that it
emerges from muscle activity or other noise sources at one particular
electrode site. The component’s activity is not systematically related to
saccade execution or stimulus presentation and thus displays similar
variance during both, saccade and fixation intervals. Based on these
observations we used the variance difference between saccade and
fixation periods in each IC to objectively differentiate eye artifact-related
ICs from those related to neural activity and other sources.
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Plöchl et al. Characterization and correction of eye artifacts
the third spatial component (radial) of the same single source that
explains artifacts produced by eye movements (Elbert et al., 1985;
Croft and Barry, 2000a,b; Schlögl et al., 2007):
EEG(t)observed =EEG(t)source +β1vEOG(t)+β2hEOG(t)
+β3rEOG(t).
For the model based on two spatial components, horizontal and
vertical EOG channels were obtained using a triangular mon-
tage, consisting of one electrode on the forehead (e1) and two
on the left (e2), and right (e3) infraorbital rim (cf. Schlögl et al.,
2007). For the three spatial components model, besides the trian-
gular montage a left (e4) and a right (e5) temporal electrode were
included to generate the radial component (Elbert et al., 1985)
according to e1/2+(e2 +e3)/4−[(e4 +e5)/2].
The coefficients for both models were obtained from the
eye(lid) movements in the pre-experimental periods described
above. The subjects’ individual coefficients were then applied to
the data of the experimental conditions in order to correct eye
movement-related artifacts.
STATISTICS
To test for statistical significance and at the same time to con-
trol for multiple comparisons, we used a cluster-based non-
parametric permutation test that is described in detail in Maris
and Oostenveld (2007). The rationale behind this test is sum-
marized as follows: if for example the difference between two
ERPs yields 20 samples that reach significance (e.g., according to
at-test), they are more likely to represent a difference in neu-
ral processing when they occur adjacent in time, as compared
to when they occur at 20 independent time points (which rather
would suggest random fluctuations in the signal). If at the same
time significant values are also observed in several neighbor-
ing electrodes, the likeliness of these values being the result of
neural activity is even higher. Following this rationale, ERP dif-
ferences or time-frequency differences that exceed a predefined
threshold (here two standard deviations from the mean) are clus-
tered and summed across adjacent channels, time points and,
for time-frequency analysis, across frequency bins. By randomly
exchanging conditions in a random subset of subjects, i.e., flip-
ping the sign of the observed values in both conditions before
averaging and clustering, an alternative observation is obtained.
Repeatingthisproceduremultipletimes(n=1000) yields a ref-
erence distribution under the null hypothesis. It now can be tested
how often clusters of the observed size are expected to randomly
occur under the null hypothesis. Additionally, for statistical com-
parisons between multiple factors we used repeated-measures
ANOVA.
RESULTS
STIMULUS RESPONSE
We aim at investigating the impact of different types of eye
movement artifacts on EEG data and evaluate the efficiency of
regression and ICA-based artifact correction methods. As a ref-
erence for distinguishing neural activity from confounds induced
by ocular artifacts we first assessed EEG responses to a standard
visual stimulus during fixation. The stimulus of choice consisted
of black and white contracting rings, because they are known
to generate both, a clear visual ERP and a distinct gamma band
response in human EEG and MEG signals (Hoogenboom et al.,
2006; Fries et al., 2008; Koch et al., 2009).
The results for the nose-referenced data are shown in
Figure 3A. The average ERP over occipital electrode sites (top
panel) displays a transient visual response followed by a pro-
longed increase in amplitude, which lasts throughout the remain-
der of the trial. The slow signal drift observed prior to stimulus
onset is most likely due to eye movements in previous trials,
which due to the high-pass properties of the recording system
FIGURE 3 | Visual response. (A) Nose-referenced data. During fixation
trials the average ERP over occipital electrode sites displays a clear visual
response (upper panel), i.e., an early transient response, followed by a
prolonged increase in amplitude. In the gamma frequency range
(30–100 Hz, middle panel) power increases from about 180 ms until the end
of the trial. Unlike similar responses in earlier studies at source level the
power increase here is not restricted to a defined frequency band over its
whole time course but also spreads over the whole gamma range in the
later portion of the trial. The power increase in the lower frequency range
(0–30 Hz, l ower panel) broadens in bandwidth coinciding with the transient
response in the ERP.(B) Average-referenced data. Similar to (A) the ERP
displays an early transient and later prolonged response; however, the
signal is smoother with lower amplitudes (upper panel). Compared to the
nose referenced data, the prolonged response in the gamma frequency
band is now more pronounced and confined to a relatively narrow
frequency range (middle panel). Similarly the early visual response in the
low frequencies is more pronounced.
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Plöchl et al. Characterization and correction of eye artifacts
cause the signal to slowly drift back toward its initial value. We
observed a steady increase in power for frequencies below 5 Hz
(Figure 3A, bottom panel), which reflects the general positive
trend of the signal over the whole trial (see also Figure 3A top
panel). Between approximately 80–160 ms a peak in the alpha
range(8–14Hz)appearstobepartiallyoccludedbythelowerfre-
quency power increase. Due to its time and frequency range this
alpha peak can be attributed to the transient visual response in
the ERP. In the gamma band (Figure 3A, middle panel) power
increases up to 20% with respect to baseline (<0 ms). In accor-
dance with previous findings the early (150–250 ms) and the late
part (380–400 ms) of this response are confined to a frequency
range between ∼50–80 Hz. However, in the time interval between
250 and 380 ms the bandwidth of the power increase broadens
and expands over entire gamma frequency range (30–100 Hz).
As this latter finding differs from earlier studies using MEG,
ICA, and invasive animal recordings (Hoogenboom et al., 2006;
Fries et al., 2008; Koch et al., 2009) we average-referenced the
data to make it more comparable to these reference free meth-
ods (Nunez and Srinivasan, 2006).Theresultsareshownin
Figure 3B: the slow signal drift in the ERP disappears and
the peak amplitude of the early visual response is decreased
(Figure 3B top panel). Moreover, the early visual response
becomes more pronounced in the low frequencies (<30 Hz)
(Figure 3B bottom panel) and extends to the lower gamma fre-
quency range (<40 Hz, Figure 3B middle panel). Similarly the
prolonged gamma band response is substantially increased and
confined to a well-defined frequency range that is in concordance
with earlier studies using source level analysis (Hoogenboom
et al., 2006; Fries et al., 2008; Koch et al., 2009). As a result
the visually evoked gamma increase now becomes distinguish-
able from the transient and broadband increase in gamma activity
(∼30–100 Hz; see also section “Microsaccades”) that is observed
in association with fixational eye movements (Fries et al., 2008;
Yuval-Greenberg et al., 2008).
CORNEO-RETINAL DIPOLE MOVEMENT
Large ocular movements result in prominent transients and off-
sets in the EEG signal. These are caused by an orientation change
of the eyeball and thus of the corneo-retinal dipole produced
between the negatively charged retina and the positively charged
cornea. The impact of corneo-retinal dipole changes on the EEG
signal is illustrated in Figure 4.
Figure 4A shows the ERPs for small (∼11.5◦, gray traces) and
large (∼23◦, black traces) vertical saccades measured at a mid-
frontal electrode (Figure 4A inset, red circle). Figure 4B shows
the same for saccades in the horizontal axis when measured at a
right temple electrode (Figure 4B inset, red circle). Depending on
whether the cornea moves toward or away from the electrode the
signal displays prominent positive and negative offsets, respec-
tively. Within the investigated range, the relationship between
movement size and corneo-retinal dipole offset is roughly lin-
ear, that is doubling saccade size results in a signal offset of twice
the amplitude. Offset topographies are presented in the right col-
umn of Figures 4A and B, which illustrate the offset amplitude
for different movement directions across the scalp normalized to
the mid-frontal (vertical movements) or right temple electrode
(horizontal movements). If linearity holds for all electrode sites,
the only difference between saccades in the same direction but
of different sizes should be in signal amplitude, while the gen-
eral topographic pattern (i.e., the normalized topography) should
stay the same. A direct comparison using a cluster-based per-
mutation test did not reveal any significant differences between
the normalized topographies of small and large saccades (p>
0.05, Figure 4C, middle and left column). Thus, the relationship
between saccade size and signal offset can be considered linear
over all electrode sites.
To compare movements of the same size but opposite horizon-
tal directions, we mirrored the topographies of leftward saccades
along the midline and subtracted them from the topographies
of rightward saccades. Again, we did not observe significant
topographic differences (Figure 4C, upper right). In the vertical
dimension, however, a change in movement direction results in a
significant difference in voltage, which extends over all electrode
sites (p<0.05, Figure 4C bottom right).
EYELID-INDUCED ARTIFACTS
Blinks occur spontaneously or can be elicited at will. During
blinking the eyelid slides down over the cornea, which is posi-
tively charged with respect to the forehead. Thereby the lid acts
like a “sliding electrode,” short-circuiting the cornea to the scalp
and producing artifacts in the EEG signal (Barry and Jones, 1965;
Matsuo et al., 1975; Antervo et al., 1985; Lins et al., 1993a,b).
Blink artifacts are easy to identify even in raw data and show
a topographic distribution mostly over frontal electrodes. As
shown in Figure 5A and demonstrated in earlier studies (e.g., Lins
et al., 1993a,b), their amplitude differs (p<0.01) between volun-
tary blinks (obtained from the pre-experimental procedure) and
spontaneous blinks (obtained from excluded trials of the main
experiment), while their normalized topographies, and thus their
propagation factors onto the scalp, do not (Figures 5A and B).
Although blink artifacts are well-known in cognitive research,
other eyelid induced artifacts occurring during and after saccades
are often neglected. As illustrated in Figure 4 during upward sac-
cades, the corneo-retinal dipole offset reaches its maximum when
the saccade ends (time 0 ms, =fixation onset). However, after
this offset change, a smaller second change of opposite polar-
ity can be observed. This latter change exceeds saccade duration
and it is known to be produced by eyelid movements that go
along with the saccade (Barry and Jones, 1965; Chioran and Yee,
1991). More specifically, saccades are accompanied by ballistic
eyelid movements, so called eyelid-saccades, which occur in syn-
chrony with the rotation of the eyeball and therefore are not
distinguishable from the corneo-retinal dipole offset in the raw
data. In other words, during upward saccades for instance, eyelid
and eyeball move upwards with approximately the same speed.
However, after the termination of both, eye- and eyelid saccades,
the eyelid continues to slide more slowly for another 30–300 ms
and produces a signal change that is observable particularly after
upward saccades (Barry and Jones, 1965; Becker and Fuchs, 1988).
In contrast to eyelid artifacts that co-occur with the saccade, this
post-saccadic signal change, although well-established in opthal-
mology, is rarely mentioned or described in relevant publications
in cognitive sciences (cf. Keren et al., 2010; Dimigen et al., 2011).
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Plöchl et al. Characterization and correction of eye artifacts
FIGURE 4 | Corneo-retinal dipole offsets. (A) Left: ERPs during small
(gray traces) and large (black traces) up- and downward saccades as
measured at a fronto-central electrode (inset, red circle). The traces show
that amplitude and duration of corneo-retinal dipole offsets scale about
linearly with saccade size. Right: The normalized topographies during small
and large saccades. (B) Small and large horizontal saccades. Same
conventions and results as in (A).(C) Topographic differences between
different types of saccades: The normalized topographies of small and
large saccades in the same vertical (left column) and horizontal (middle
column) direction do not display any significant differences, indicating that
the linear relationship between saccade size and signal offset holds for all
electrode sites. The same is observed for horizontal movements of the
same size but opposite directions (right column, top). In the vertical
dimension, however, downward saccades produce a significantly larger
offsets than upward saccades of the same size (right column, bottom;
significant electrodes marked by bold black dots).
To confirm previous findings and to investigate how this
post-saccadic eyelid artifact affects our data, we compared the
difference between the signal amplitude measured at a frontal
electrode (see inset Figure 5A) at the time of saccade offset and
the signal amplitude at the same electrode but 100 ms later, when
the eyelid-induced signal change has reached its final level (as
for example seen in Figure 4A). A Two-Way repeated-measures
ANOVA (three movement types: periphery to center, center to
periphery and periphery to periphery; four movement direc-
tions: up, down, left, and right) revealed that amplitude differ-
ences between saccade offset and eyelid offset depend on both,
movement type (F=25.63, p<0.001) and movement direction
(F=42.47, p<0.001). We observed significant amplitude dif-
ferences between long and short saccades (p<0.001) and found
that eyelid-induced amplitude changes were significantly larger
(p<0.01) for upward saccades than for all other movement
directions. Together with the finding that there is an interac-
tion between type and direction of the movement (F=2.55,
p<0.01) this confirms earlier reports that eyelid-induced ampli-
tude changes are most prominent after upward directed eye
movements (Barry and Jones, 1965; Becker and Fuchs, 1988;see
also Figure 4A).
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Plöchl et al. Characterization and correction of eye artifacts
FIGURE 5 | Eyelid-induced signal changes. (A) ERP traces for voluntary
(gray) and spontaneous (black) blinks measured at a frontal electrode (inset,
red circle). Voluntary blinks are of longer duration and result in higher
amplitudes than involuntary blinks. Note that blink onset as defined by the
eye tracker (time 0, vertical dashed line) corresponds to the point at which
the pupil is not visible anymore. The actual eyelid movement already starts
around 100 ms earlier, when the signal deflects from the zero line
(horizontal dashed line). (B) Although spontaneous and voluntary blinks
differ in amplitude and duration, they share the same topographic
pattern (i.e., the normalized amplitude distribution across the scalp).
(C) Topographic patterns of corneo-retinal dipole offsets related to upward
saccades, blinks, and post-saccadic eyelid movements (upper row) and the
differences between them (lower row). Bold black dots indicate electrode
sites with statistical significant differences. The results show that
corneo-retinal dipole offsets produce a topographic pattern that differs from
both, blinks and post-saccadic eyelid movements, while no differences
were found between the latter two. This suggests that blinks and
post-saccadic eyelid movements are produced by the same
electrophysiological source.
Next, we compared the normalized topographies of blink
artifacts, corneo-retinal dipole offsets and post-saccadic eyelid
artifacts. As blink- and saccade-related eyelid artifacts are pro-
duced by the same mechanism, namely the eyelid sliding over
the cornea, their activity projects to the scalp with the same
topographic pattern (Figure 5C). Topographies related to corneo-
retinal dipole offsets on the other hand differ from both, those of
blinks and those of post-saccadic eyelid movements, with signif-
icant differences clustered around the central and fronto-lateral
regions (p<0.05, Figure 5C). Effectively these results indicate
that eyelid induced artifacts and corneo-retinal dipole offsets arise
from two different electrophysiological mechanisms that cannot
be modeled as a single source.
SPIKE POTENTIAL
In early studies the saccadic spike potential has been described
as a “monophasic potential appearing just before saccade onset”
(Thickbroom and Mastaglia, 1985). However, concordant with
earlier studies (Riemslag et al., 1988; Nativ et al., 1990; Keren et al.,
2010), high-pass filtering the data at 10Hz and thereby removing
corneo-retinal dipole offsets reveals that the saccadic spike poten-
tial actually displays a biphasic waveform, starting around 5 ms
prior to saccade onset and consisting of a larger positive deflection
followed by a smaller negative deflection (Figure 6A). Note that in
the present study, as compared to the above-mentioned ones, the
polarity of the waveform is inverted. This is because we describe
the properties of the saccadic spike potential at scalp electrodes
rather than at EOG channels (Keren et al., 2010)andbecausewe
used a nose reference, while other studies referenced to an elec-
trode attached to the temporal bone (Thickbroom and Mastaglia,
1985; Riemslag et al., 1988). Since in the unfiltered data the neg-
ative deflection of the saccadic spike potential is largely occluded
by the corneo-retinal dipole offset (Figure 6A, insets) the follow-
ing analysis will only focus on the positive peak of the saccadic
spike potential.
To investigate amplitude and topography of the saccadic spike
potential for saccades of different sizes and directions, we relate
the data to a 5 ms baseline before the onset of the spike potential
(i.e., −10 to −5 ms relative to saccade onset). Figure 6B shows the
topography of the positive peak of the spike potential averaged
over all saccade directions. When the data is average-referenced,
the topographic pattern stays the same while the magnitude is
shifted from central-parietal electrodes to electrode sites sur-
rounding the eyes (Figure 6B,cf.Keren et al., 2010). For the
remainder of the analysis of saccadic spike potentials we used
nose-referenced data exclusively.
To compare the topographic pattern of spike potentials
between eye movement directions we normalized the respective
topographies to the maximum amplitude over all saccade direc-
tions. The difference between spike potentials accompanying up-
and downward saccades shows that during upward saccades at
almost all electrodes the measured potential is significantly lower
(p<0.05) than during downward saccades (Figure 6C,left).A
possible explanation is that at the onset of a downward sac-
cade the eye, and thus the positive pole of the corneo-retinal
dipole, is directed upward, consequently leading to a more pos-
itive topography as compared to the onset of upward saccades
where the positive pole of the corneo-retinal dipole is initially
directed downwards. This is also supported by the observation
that topographic differences between left- and rightward saccades
become significant (p<0.05) at electrodes around the eyes and
thus close to the corneo-retinal dipole (Figure 6C, middle). We
found no differences between the topographic patterns of ipsi-
and contralateral saccades (i.e., the topography of leftward sac-
cades versus the horizontally flipped topography of rightward
saccades, Figure 6C,right).
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Plöchl et al. Characterization and correction of eye artifacts
FIGURE 6 | Saccadic spike potential. (A) After removal of the
corneo-retinal dipole by high pass filtering the data at 30 Hz, the spike
potential displays a biphasic shape at all scalp electrodes (colored traces).
(Continued)
FIGURE 6 | Continued
Without filtering the ERP traces for up- (upper inset) and downward (lower
inset) saccades show that the negative deflection of the spike potential is
largely occluded by the manifold larger corneo-retinal dipole offset.
(B) Average referencing reduces the amplitude of the spike potential at
scalp electrodes and leads to increased negativity around the eyes.
(C) Differences of topographic patterns for spike potentials related to
saccades in the same axis but opposite directions. Comparing spike
potentials related to up- and downward saccades of the same size results
in significant amplitude differences at almost all scalp electrodes, indicating
that downward saccades produce higher spike potential amplitudes. The
difference between left and rightward saccades yields significant values at
electrode sites close to the eyes. However, no significant differences were
found between spike potentials related to ipsi- and contralateral saccades,
suggesting that the topographic differences for saccades with the same
size and along the same axis may be not so much related to the spike
potential’s amplitude itself but rather to eye position (i.e., the direction of
the corneo-retinal dipole) before saccade onset. (D) Spike potential
topographies for different saccade directions (columns) and sizes (rows). A
two-way ANOVA reveals that peak amplitudes of the spike potential are
higher for down- than for upward saccades while they are not significantly
different between left and rightward saccades. Smaller saccades from the
periphery to the center result in higher spike potential amplitudes than
saccades of the same size but performed from the center to the periphery.
Surprisingly saccades from the periphery to the center do not show
significant differences to long saccades (i.e., saccades performed from the
periphery across the center to another peripheral location). This indicates
that the peak amplitude of spike potentials may depend on initial eye
position, rather than on saccade size.
Next we compared how the amplitude of saccadic spike poten-
tials changes depending on the type of eye movement. Figure 6D
shows the respective amplitudes and topographies for different
saccade sizes, directions and initial eye positions, that is up-,
down-, left- and rightward saccades performed from the periph-
ery to the center, from the center to the periphery and from
the periphery to the opposite peripheric region (i.e., long sac-
cades). For each of these classes we determined the highest peak
at central-parietal electrodes during a time period ranging from
−5to+10 ms relative to saccade onset. A Two-Way repeated-
measures ANOVA revealed that the amplitude of the saccadic
spike potential depends on both direction (F=13.05, p<0.001)
and type of the movement (F=29.06, p<0.001). It signifi-
cantly differs between vertical and horizontal saccades (p<0.05,
Bonferroni corrected) and movements within the vertical dimen-
sion (i.e., upward and downward saccades, p<0.001), but not
between left- and rightward saccades. The amplitude of sac-
cadic spike potentials has been shown to increase non-linearly
with saccade size up to 1.28◦before reaching its final maximum
(Armington, 1978). However, as saccade sizes in our experiment
exceeded this threshold, we did not expect any amplitude differ-
ences between spike potentials related to long (∼23◦)andshort
(∼11.5◦) saccades. But interestingly the amplitudes observed for
short saccades from the center to the periphery were significantly
different from those accompanying long saccades (p<0.001).
This difference, however, cannot be attributed to saccade size, as it
was not observed between long saccades and short saccades from
the periphery to the center.
To check whether this rather surprising finding could be a
result of filter-induced slow signal drifts in the recording, as
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Plöchl et al. Characterization and correction of eye artifacts
described above (i.e., saccades to peripheral screen positions are
generally larger and therefore might produce larger drifts when
the signal returns back to baseline), we repeated the analysis after
high pass filtering the data at 10 Hz, and in this way eliminating
such drifts. However, using filtered data yielded the same results
as using unfiltered data. This suggests that for saccades larger than
∼11.5◦the amplitude of the saccadic spike potential depends on
the initial eye position, rather than on saccade size per se.
MICROSACCADES
Microsaccades are small eye movements that occur during fixa-
tion. They distinguish themselves from regular saccades not only
in amplitude but also in that they are performed involuntarily.
In the fixation condition we detected a total of 4252 microsac-
cades over all trials and subjects, which corresponds to an average
of 1.07 microsaccades per trial. Note that although sometimes
other or additional criteria (e.g., larger or smaller amplitude
range, involuntary performance etc.) are applied, here we defined
all eye movements with amplitudes between 0.1 and 1◦of visual
angle as microsaccades. To ensure that the detected movements
were indeed microsaccades and not noise in the eye move-
ment recordings, we investigated the relationship between their
amplitude and velocity. In agreement with previous descriptions
(Martinez-Conde et al., 2004), the microsaccades detected in our
data follow a linear relationship when plotted on a log-log scale
(i.e., the “main sequence,” Figure 7A).
Microsaccades typically occur with a frequency of 1–2 Hz
(Engbert, 2006). The probability of their occurrence, however, is
not equally distributed over time but follows a typical temporal
pattern with respect to stimulus onset: after stimulus presentation
their rate initially decreases to its minimum at around 150 ms.
Thereafter it rapidly increases again reaching its maximum at
about 350 ms (Engbert and Kliegl, 2003; Dimigen et al., 2009).
When we investigated the distribution of microsaccades over all
fixation trials and all subjects, we found that it matched this
pattern very closely (Figure 7B).
To study the impact of microsaccades on the EEG signal, we
segmented the data into epochs time-locked to microsaccade
onset. The ERP of these epochs displays the same biphasic wave-
form as the saccadic spike potential of larger saccades, although
its amplitude is substantially smaller (Figure 7C). In the time-
frequency analysis of our data this pattern manifests itself as
a transient burst between 40 and 100 Hz (Figure 7D), which
is in concordance with earlier studies (Yuval-Greenberg et al.,
2008; Keren et al., 2010). Altogether these findings confirm, that
microsaccade-induced confounds in the EEG are mainly caused
by spike potentials occurring at microsaccade onset, while ori-
entation changes of the corneo-retinal dipole only play a minor
role.
ARTIFACT CORRECTION
In order to evaluate the efficiency of different approaches to eye
artifact correction we compared the performance of a two and
a three component regression model (Elbert et al., 1985; Schlögl
et al., 2007)andICA(Jung et al., 2000).
In order to objectively distinguish eye movement-related
from non-eye-movement-related ICs we employed a selection
FIGURE 7 | Microsaccades. (A) Mainsequence. Plotting microsaccade
amplitudes against microsaccade velocities results in a straight line on a
log/log scale (i.e., the mainsequence). This relationship is a signature for
ballistic eye movements, thus confirming the physiological origin of the
microsaccades detected here. (B) Histogram of the microsaccade
distribution over all fixation trials. After stimulus onset the frequency of
microsaccades decreases, followed by a rebound starting at around 200 ms
and peaki ng 370 ms after stimulus onset. (C) ERP aligned to microsaccade
onset. At scalp electrodes microsaccades display a similar biphasic pattern
as the saccadic spike potential, suggesting that the most prominent
contribution of microsaccades to the signal measured on the scalp is
produced by spike potentials going along with eye movement.
(D) Time-frequency signature of microsaccades. The sharp peak of the
microsaccade-related spike potential results in a transient broadband power
burst that spans over the entire gamma frequency range (30–100 Hz).
(E) Reduction of microsaccade-related artifacts in the time domain. The
ICA-based correction procedure proposed here diminishes the
microsaccade-related spike potential to about one third of its original
amplitude. (F) Reduction of microsaccade-related artifacts in the frequency
domain. Corresponding to what was observed for the ERP, the correction
procedure substantially reduces the spike potential-related frequency
signature in the gamma band.
procedure based on eye tracking information. More specifically,
we compared every IC’s activation during saccade and fixation
periods. If the respective IC displayed a higher activation during
saccades than during fixation, it was classified as eye movement-
related and rejected from the data (see section “Independent
Component Analysis” and Figure 2 for more details). Following
this procedure we found between 3 and 11 artifactual ICs for
each subject, accumulating to a total number of 74 (out of 896)
rejected ICs.
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Plöchl et al. Characterization and correction of eye artifacts
To test how well the automated selection algorithm performed
in comparison to visual selection by experienced researchers, we
asked two independent experts to tag all ICs in our data that
they considered as eye movement-related. The classification was
done in three steps: (1) we asked both experts to identify eye
movement-related ICs solely based on their respective topogra-
phies. Over all subjects and components, Expert 1 classified 36
ICs as eye movement-related, while Expert 2 identified 41 ICs as
related to ocular artifacts. (2) Next we asked the experts to repeat
their evaluation but this time based on both, the ICs’ topogra-
phies and their activations during three example trials, each of
which contained an eye movement in a different direction (up,
down, and left). Given the additional information about the ICs’
activations, Expert 1 rated 16 additional ICs as eye movement-
related. Similarly, Expert 2 tagged 16 more ICs as eye artifacts but
additionally revised his previous judgment about four compo-
nents, which he removed from his selection. Now, in total 52 ICs
were tagged by Expert 1 and 53 ICs by Expert 2. Notably, apart
from the one additional IC identified by Expert 2, both experts’
selection of eye movement ICs was identical. This strongly sug-
gests that including IC activations as a decision criterion leads to
a substantial improvement in effectiveness and reliability of IC
classification. (3) In a last step we determined all ICs that had a
variance ratio (i.e., variance of saccade intervals vs. variance of fix-
ation intervals; see section “Independent Component Analysis”
and Figure 2) above one and that the respective expert had not
tagged as eye movement-related before. For these ICs, next to
their topographies, we now provided the activations during all
available saccade trials and again asked the experts to classify them
as either eye movement-related or unrelated. Now having a large
number of trials available, Expert 1 tagged another 16 ICs and
Expert 2 another 15 ICs. Thus, in total both experts rated 67 ICs
out of 896 as eye movement-related and their assessment was now
perfectly congruent.
Out of the 67 ICs identified by human scorers, 64 ICs were
also detected by the automated selection procedure. Additionally
the algorithm tagged 7 ICs that did not conform to the experts’
assessment. However, as 6 of these 7 ICs were from only one sub-
ject and their topographies peaked in neighboring regions of the
scalp, we assume that these 6 ICs may have entered the selection
due to unusual activity and/or noise in this particular subject’s
data.
For further quantification of the algorithm’s efficiency and
to test the adequacy of the pre-determined ratio threshold
of 1.1 (Figure 8A), we performed a receiver operating char-
acteristic (ROC) analysis (Figure 8B; see figure captions for a
description of the method), where ICs tagged by human experts
served as reference (true positives). We observed an area under
curve (AUC) value of more than 0.99, which indicates that
eye movement-related ICs are almost perfectly separable from
other ICs solely based on their saccade/fixation variance ratios.
The optimal threshold for this separation is defined by the
point at which further lowering the threshold would include
more false positives than true positives and is indicated by the
green arrow in Figure 8C. The respective saccade/fixation vari-
ance ratio at this point is 1.11 and the following one (i.e., the
first suboptimal threshold) has a value of 1.06. This means that
the pre-determined threshold of 1.1 turned out to be chosen
optimally for the dataset presented here.
Since it is not possible to measure uncontaminated neural
activity with EEG, the validation of correction procedures is
always suboptimal (Croft and Barry, 2000a,b). Simple inspec-
tion of corrected raw data by expert scorers as used elsewhere
(Jung et al., 2000; Joyce et al., 2004; Schlögl et al., 2007)mightbe
insufficient, as small residuals of artifacts may be not noticeable
in the raw signal. Averaging over many trials however increases
the signal-to-noise ratio and thus facilitates the detection of
such residuals. For this reason we evaluated correction perfor-
mance based on the ERP and the time-frequency response, both
time-locked to the saccade.
The results of ERP correction by using 2 (green traces) and
3 (black traces) model regression and the proposed ICA-based
procedure (red traces) are shown in Figure 9.Artifactcorrection
by regression fails to completely remove the offset produced by
eye movements or blinks. In any case, apart from spike poten-
tial suppression, the correction by the regression model with two
spatial components performs better than the one with three spa-
tial components. This probably reflects an excessive introduction
of correlated neural activity to the EOG measurements due to the
use of temporal channels for the calculation of the axial compo-
nent of the model. Because regression coefficients were obtained
in a period including both eye movements and blinks, regres-
sion fails to entirely remove offsets because the regressors that
are fit to the data are based on both, corneo-retinal dipole off-
sets and eyelid artifacts. As a result the weights that are assigned
to each electrode for all movement directions constitute a com-
promise between these two artifacts. For more posterior channels
(Figure 9, 2nd and 3rd row) this results in over-correction for
blinking and under-correction for saccades, as blink peaks prop-
agate less to the back than saccade offsets do (see Figure 5C).
ICA on the other hand independently removes both, corneo-
retinal dipole offsets and eyelid artifacts, and thus accounts for the
fact that their relative contribution to the measured signal varies
for different movement directions. However, except for upward
saccades ICA failed to entirely remove the spike potential at sac-
cade onset, but nevertheless reduced it by about 75% depending
onsaccadesizeanddirection(Figure 9, red traces). The visual
response is still clearly seen in occipital channels but due to vari-
ations in saccade duration it does not display its distinctive shape
(see Figure 3) but manifests itself as a flat and broad peak at
around 180 ms.
To check whether the procedure might over-correct the data
and thus remove neural signals along with eye movement arti-
facts, we compared the raw and the corrected data during fixation
trials (Figure 9, 5th column). We observed a significant difference
(p<0.001, shaded area) between ERPs of raw (blue traces) and
ICA corrected data (red traces), comprising a time interval from
198 ms after stimulus onset to the end of the trial. However, the
distribution of microsaccades over all trials (Figure 9,5thcolumn
bottom) and the observation that after correction microsaccade-
related spike potentials are reduced to about one third of their
initial value (Figure 7E), suggest that a significant part of this
difference can be attributed to the reduction of microsaccade-
related artifacts, rather than to removal of neural activity. To test
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Plöchl et al. Characterization and correction of eye artifacts
FIGURE 8 | Evaluation of the IC selection procedure. (A) Distribution of
saccade/fixation variance ratios. The magnification in the inset illustrates that
the ratios are not clearly bimodally distributed. Therefore the threshold that
optimally separates eye movement-related ICs from non-ocular ICs is difficult
to determine. Based on heuristics, that is after inspecting IC activations that
had a ratio above one and that we considered likely to be eye
movement-related, we set the threshold to a ratio of 1.1. (B) ROC analysis.
ROC curves are graphical illustrations of how well two different classes (here:
eye movement-related ICs vs. non-eye movement-related ICs) can be
separated depending on the threshold of the discrimination criterion (here:
the ratio between the variance in saccade and fixation intervals). Each point
on the blue curve represents the saccade/fixation variance ratio of one IC
starting with the highest (63.71) in the lower left corner and ending with the
lowest (0.04) in the upper right corner. If ICs could be unequivocally
separated into being eye- or non-eye-related solely based on their variance
ratio, lowering the threshold would include more and more eye movement
ICs (as determined by expert tagging) until a true positive rate of 100% is
reached. Subsequently by further lowering the threshold more and more
non-eye movement-related components would be included in the selection.
In this case the blue curve would be identical with the red line. Conversely, if
the variance ratio would not provide any information about the IC’s relation to
eye movements, the blue curve would follow the black dashed line. The area
under curve (AUC), which is obtained by computing the area between the
blue and the black dashed line, quantifies how well eye movement-related
ICs are separated from other ICs only based on their saccade/fixation
variance ratio. An AUC value of 1 indicates perfect discrimination and a value
of 0.5 indicates random performance. Here we observed an AUC value of
more than 0.99. (C) ROC curve detail. The green arrow indicates the the
optimal threshold for separating the ICs into two classes. It is given by the
point at which further lowering the threshold would include more false
positives than true positives. Here it has a value of 1.11 and is thus very close
to our pre-determined threshold. The red arrow indicates the threshold at
which all eye movement-related ICs are included in the selection. The
corresponding ratio is 0.99.
this assumption we repeated the analysis with only those trials in
which we did not detect any microsaccades. As seen in the right-
most column in Figure 9 the difference between raw (blue) and
ICA corrected data (red) is now smaller, especially in central and
occipital regions, and only becomes significant 278 ms after stim-
ulus onset, which is 80 ms later than what we observed in trials
including microsaccades. The topography of the remaining dif-
ference strongly resembles the topography of the saccadic spike
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Plöchl et al. Characterization and correction of eye artifacts
FIGURE 9 | ERP correction. ERP traces for uncorrected data (blue), data
corrected based on two (green) and three (black) component regression
models and ICA corrected data (red). Rows show the ERPs measured at frontal
(top), central (middle), and occipital (bottom) scalp locations and the red circles
in the head plots on the left indicate the respective electrode sites. Columns
correspond to up-, down-, and rightward saccades, blinks and fixation trials
with and without detected microsaccades, respectively. Time 0 represents
saccade and blink onset as determined by the eye tracker and stimulus
presentation in fixation trials. The traces suggest that regression tends to
over- or under-correct the data. ICA on the other hand efficiently removes
corneo-retinal dipole offsets and eyelid artifacts, while the visual response is
still clearly seen in occipital channels. However, ICA fails to entirely remove
the spike potential at saccade onset but still reduces it substantially. In the
fixation condition including microsaccades the raw and the ICA corrected data
display significant differences (shaded area) in the time interval after 198 ms,
but the distribution of microsaccades over all trials (bottom row) suggests that
a significant portion of these differences can be attributed to the reduction of
microsaccade-related artifacts. This is supported by the observation that in
fixation trials without detected microsaccades, the difference between raw
and ICA corrected data is smaller and only becomes significant after 278 ms.
The topography of this difference resembles the one of the saccadic spike
potential and may therefore be related to undetected microsaccades.
potential. We therefore conclude that a large part of this differ-
ence may be due to mircosaccades that are still present in the data
but were not detected in our eye tracking data. Additionally, anal-
ogous to average referencing, ICA correction may reduce global
noise and power (see also Figure 2). This may also be the reason
why during fixation trials the regression model with a third spa-
tial component spanning across a large part of the brain is more
similar to the ICA corrected than to the raw data (Figure 9,5th
column).
In summary, rejecting ICA components based on eye track-
ing information resulted in complete removal of corneo-retinal
dipole offsets and eyelid artifacts from the ERP. It also reduced
confounds related to saccadic spike potentials to a very large
extent, without substantially affecting signals from neural sources.
Nevertheless, confounds that are not strictly time-locked to
the saccade or that are restricted to a certain frequency range
may go unnoticed in the ERP. Therefore we evaluated eye track-
ing based IC rejection also with respect to its efficiency in the
frequency domain. The results are seen in Figure 10:inthe
high frequencies (30–100 Hz) the uncorrected data (left column)
display the typical broadband gamma burst-related to the spike
potential at saccade onset. Consistent with the topography of
this spike potential this burst is most pronounced at central and
occipital channel locations. Additionally, as a result of small cor-
rectional saccades, a similar but much weaker burst is observed
about 180 ms after fixation onset. In frequencies below 10 Hz the
corneo-retinal dipole offset leads to a general power increase,
which is most pronounced at frontal electrode sites. In concor-
dance with the observation for ERP data, ICA correction (left
column) completely removes power changes related to corneo-
retinal dipole offsets and eyelid artifacts, and reduces the high-
frequency correspondent of the saccadic spike potential by about
85% (Figure 10A). Note that in concordance with earlier stud-
ies (Keren et al., 2010) a similar reduction was also observed for
microsaccadic spike potentials (Figure 7F).
Investigating the data at occipital electrode sites on a lower
color scale provides a more detailed view on the properties of
eye movement confounds and the impact of the correction pro-
cedure (Figure 10B). In the uncorrected data (left column) the
broadband gamma burst of the saccadic spike potential extends
into the low frequency range and together with the power increase
related to corneo-retinal dipole offsets, covers the early transient
visual response that was observed in the ERP. Additionally, the
second broadband burst largely occludes the later visual response
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Plöchl et al. Characterization and correction of eye artifacts
FIGURE 10 | Correction in the time-frequency domain. (A) Correction of
eye movement artifacts in the high (larger boxes) and low (smaller boxes)
frequency ranges at frontal (top panel), central (second panel), and occipital
(bottom panel) electrode sites. Left column: In the high frequencies
(30–100 Hz) the uncorrected data display the typical broadband gamma
burst-related to the spike potential at saccade onset. Corresponding with the
topography of the spike potential this burst is most pronounced at central and
occipital channel locations. As a result of small correctional saccades, a
similar but much weaker burst is observed about 180ms after fixation onset.
In the low frequencies (<30 Hz) the corneo-retinal dipole offset leads to a
prolonged increase in power below 10 Hz, which is starting at saccade onset
and manifests itself most prominently at frontal electrode sites. Right
column: In concordance with the observation for ERP data, ICA correction
significantly reduces the spike potential-related gamma burst and completely
removes the power increase related to corneo-retinal dipole offsets.
(B) Correction of eye movement artifacts at occipital electrode sites on a
more detailed color scale. Left column: The second broadband burst in the
gamma range, which is caused by correctional saccades occludes the
prolonged visual response we found earlier during fixation trials. Similarly,
with the more detailed color scale confounds produced by the spike potential
are also observed in the low frequency range and together with the
corneo-retinal dipole-related power increase, the early visual response that is
observed in the ERP is largely occluded. Right column: The more detailed
color scale confirms that ICA completely removes corneo-retinal dipole
induced power changes, while the spike potential still visible for both,
task-related and correctional saccades. However, the removal of
corneo-retinal dipole offsets and reduction of the spike potential renders the
visual responses clearly visible in both the low and high frequencies.
in the gamma range (see Figure 3). In the corrected data on the
other hand, both, the early transient (after 70 ms) and the later
prolonged visual response (after around 200 ms) become clearly
visible in the low and high frequencies, respectively (Figure 10B,
right column).
Again, we checked whether the correction procedure affects
the signal during fixation trials, as in the absence of eye move-
ments there should not be any discrepancies between the raw
and the ICA corrected data. Figure 11A shows the results. In the
uncorrected data (left) we observed a prominent gamma power
increase at central electrode sites starting at around 250 ms after
stimulus onset. That corresponds to the time and frequency range
of the bandwidth increase of the visual gamma band response in
occipital channels, which was described above (Figure 3). In the
corrected data this broadband gamma increase disappears and
the visual response is confined to a frequency band between 50
and 85 Hz similar to what was observed in the average-referenced
data and reported in earlier studies. Moreover, the peak of this
response is now also visible in central channels. In the lower fre-
quency range the corrected data reveal a distinct peak at 9 Hz, cor-
responding to the early transient response in the ERP. Comparing
the difference between corrected and uncorrected data (right col-
umn) with the distribution of microsaccades indicates that the
significant portions of the observed differences (original color,
non-significant bins are grey shaded) follow the pattern of the dis-
tribution very closely. The power drop-off in gamma band after
stimulus onset seems to be caused by a drop in microsaccade
rate relative to the pre-stimulus interval, which here serves as the
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Plöchl et al. Characterization and correction of eye artifacts
FIGURE 11 | Differences between uncorrected and ICA corrected time
frequency data during fixation trials. (A) Same conventions as in
Figure 9. Left column: In the uncorrected data a prominent broadband
gamma power increase is observed at central electrode sites. Occipital
channels display prolonged activity in the gamma range corresponding to
the late visual response as described in Figure 3. Middle column: In the
corrected data the prolonged visual gamma response is confined to a
frequency band between approximately 50 and 80 Hz and its peak now
also visible in central channels. The low frequencies reveal a distinct peak
at around 9 Hz corresponding to the early transient response in the ERP.
Right column: Comparing the difference between corrected and
uncorrected data with the distribution of microsaccades indicates that the
significant portions of the observed differences (i.e., bins in original color;
non-significant bins are gray shaded) follow the pattern of microsaccade
distribution very closely. The negative gamma power difference after
stimulus onset results from a drop in microsaccade rate relative to the
pre-stimulus interval, which here serves as the baseline. Then coinciding
with the increase in microsaccade rate after 250 ms the difference in
gamma power substantially increases. (B) In trials without detected
microsaccades the largest part of the broadband gamma power increase
disappears and smaller differences between raw and ICA corrected data
only become significant after 300 ms. Again these residual differences
display the spatial and spectral signatures of the saccadic spike potential
and therefore are likely the result of undetected microsaccades.
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Plöchl et al. Characterization and correction of eye artifacts
baseline. The rebound of microsaccade rate after 230 ms coincides
with a substantial increase in gamma power, again indicating that
the observed differences are caused by miniature eye movements
during fixation. Like with the ERP data before, we tested this pre-
sumption by repeating the analysis with only those trials in which
we did not detect any microsaccades. The results are shown in
Figure 11B: as compared to trials with microsaccades, the broad-
band power increase after 250 ms at central and occipital channels
largely disappears, thus supporting the hypothesis that it was
caused by microsaccadic spike potentials. We still find differences
between the raw and the ICA corrected data, but now being much
smaller and starting later at around 300 ms after stimulus onset.
Analogous to our observations in the ERP data we conclude that
these differences are mainly caused by undetected microsaccades,
rather than by removal of neural activity. This is strongly sup-
ported by the frequency signature and scalp distribution of the
residual difference, which very closely resemble the ones that we
observed for spike potentials and microsaccades, respectively.
DISCUSSION
We were able to confirm that eye- and eyelid movement-related
artifacts arise from several independent mechanisms and showed
that their relative contribution to the measured EEG signal
depends on the type of eye movement.
We evaluated the performance of ICA and regression-based
eye artifact correction methods. As using ICA for artifact rejection
generally poses the problem to objectively differentiate between
ICs related to artifacts and ICs related to neural activity, we
devised a selection procedure that identifies eye artifact related
ICs based on eye tracker information. Rejecting these ICs from
the data resulted in complete removal or significant reduction
of the above-described eye- and eyelid movement artifacts, while
leaving the relevant signal emerging from neural sources intact.
In contrast, both regression models tested here resulted in subop-
timal artifact correction.
Belowwewillreviewourfindingsinthelightofprevious
studies of eye movement artifacts and discuss the resulting impli-
cations for artifact correction and data analysis. We discuss why
ICA is in principle suited to extract artifacts arising from multiple
sources and why regression is likely to fail under these circum-
stances. Finally we consider neural activity that goes along with
eye movements and therefore may have an impact on the data
despite artifact correction.
EYE- AND EYELID MOVEMENT ARTIFACTS
Eyeball rotation artifacts
Eyeball rotations induce signal changes that depend on both,
movement size and direction. The polarity of these changes in
turn depend on the orientation of the corneo-retinal dipole,
whose axis closely follows the direction of gaze. The corneo-
retinal dipole itself is produced by the ionic gradient between the
apical and basal surfaces of the retinal pigment epithelium (for
a review of the underlying physiology see Arden and Constable,
2006). However,even when the retina is surgically removed, signal
changes related to eyeball rotation can still be recorded. Therefore
it is probable that other sources also contribute to the gener-
ation of the dipole, like for instance potential differences that
occur exclusively across the cornea or between the blood and the
intraocular fluids (Pasik et al., 1965).
For any given movement direction, the amplitude of the
corneo-retinal dipole offset changes roughly linearly with saccade
size. However, the magnitude of the corneo-retinal dipole itself
is far from static. It has been shown that the potential between
cornea and retina changes with illumination (Miles, 1940; Arden
and Kelsey, 1962). This change may occur on a time scale ranging
from a few milliseconds to several hours. For instance, illumi-
nation of a dark-adapted eye produces a rise in potential that
peaks after 10–15 min (Lins et al., 1993a). The respective peak
amplitude depends on both, the intensity of the light and the
duration of the dark adaptation and it is followed by a damped
oscillation that continues for hours (Arden and Kelsey, 1962).
Next to other reasons (cf. Dimigen et al., 2011), these fluctua-
tions in response to illumination changes constrain the utilization
of the EOG as an absolute measure for gaze position, because a
reliable estimation would require recalibration about every 10 s
(North, 1965). However, changes in the standing potential do
not preclude the correction of corneo-retinal dipole-related arti-
facts since the propagation of the potential through the volume
conductor is independent of its intensity (Girton and Kamiya,
1973).
More problematic in this respect is the fact that eyeball rota-
tion can be accompanied by translational or torsional movements
of the eye. Such eyeball displacements impede accurately model-
ing the potential difference between cornea and retina as a single
dipole, which in turn leads to difficulties in estimating a single
set of scalp propagation factors for a given dipole orientation.
Still, in the present study, we found that saccades of different
sizes but with the same orientation share the same topographic
pattern and thus the same propagation factors of corneo-retinal
dipole-related artifacts onto the scalp. This implies that for the
movements investigated here, possible displacements of the eye-
ball do not have significant impact on the measured signal. We
also observed that movements of opposite orientation in the hor-
izontal axis do not only result in the same (but mirror reversed)
propagation factors but that they also project to the scalp with
the same amplitude. Although we found a global difference in
amplitude between up- and downward movements, the similar-
ity of their topographic patterns suggests that this is because the
reference is not located symmetrical with respect to the plane of
eye rotation, rather than because of a difference between their
respective propagation factors.
Eyelid artifacts
Artifacts emerging from blinks, eyelid saccades, and post-saccadic
eyelid movements are produced by the same principal mech-
anism, namely the eyelid changing the resistance between the
positively charged cornea and the forehead. Accordingly lid fix-
ation severely reduces or even prevents eyelid-related potential
changes in the EEG (Chioran and Yee, 1991). Blinks are also
accompanied by an active small extorsional, downward, and
nasalward eye movement which is followed by a fast passive return
to the pre-blinking position due to passive elastic forces (Bour
et al., 2000; Bergamin et al., 2002). Yet artifacts arising from such
blink-associated eye movements are usually occluded by the lid
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Plöchl et al. Characterization and correction of eye artifacts
induced artifact and therefore only play a minor role with respect
to blink-related signal contamination.
Importantly, the distribution of eyelid artifacts at scalp level is
independent of corneo-retinal dipole orientation. As our results
show, their topographic pattern does not change across different
types of eye and eyelid movements, and their related potentials
only differ in amplitude and duration (see also Evinger et al.,
1991; Guitton et al., 1991; Lins et al., 1993b; VanderWerf et al.,
2003). Still, eyelid artifacts are interrelated with the corneo-retinal
dipole in the sense that an intact eye globe is necessary for the
production of blink potentials. Active or passive movements over
a non-metallic prosthetic eye do not result in blinking potentials
(Matsuo et al., 1975). Conversely, passive eyelid movement over
an intact eye results in electrical changes congruent with the ones
of blinking and eyelid artifacts (Matsuo et al., 1975).
Another important issue concerning the interrelation between
eye and eyelid artifacts is that saccadic eyeball rotations are
accompanied by ballistic eyelid movements, or “eyelid-saccades,”
that are performed in synchrony with saccadic eye movements
(Becker and Fuchs, 1988; Evinger et al., 1991; Guitton et al., 1991;
Helmchen and Rambold, 2007). Saccades and eyelid-saccades are
coordinated by several brainstem structures and different corti-
cal pre-motor and motor areas (Helmchen and Rambold, 2007).
Consequently, eyelid-saccade parameters are closely matched
with those of eye-saccades (Becker and Fuchs, 1988) and therefore
cannot be disentangled in the signal measured at scalp electrodes.
However, after the termination of both eye and eyelid saccades
the lid continues to slide for another 30–300 ms (Becker and
Fuchs, 1988). This post-saccadic eyelid movement produces a
returning change in offset which—in concordance with both, pre-
vious studies and our data—is exclusively observed after vertical
and oblique upward saccades (Barry and Jones, 1965). However,
based on the interpretation of source dipole modeling of eye
artifacts, other authors have proposed that post-saccadic eye-
lid artifacts could also be present for horizontal saccades (Lins
et al., 1993a). Although we do not see corresponding changes
at scalp level, we cannot discard this possibility, especially as
eyelid related ICs display offset changes also during horizontal
saccades. Note, that post-saccadic eyelid artifacts are not related
to eye movement overshoots as they could be interpreted at
first sight (Barry and Jones, 1965). This is supported by the
observation that the returning change in offset is not present
for eye movements performed with eyes closed (Huddleston,
1970).
Finally, the observation that velocity and duration profiles
of post-saccadic eyelid movements and eyelid movements dur-
ing opening the eyes after blinking are very similar, further
argues for the same underlying mechanism (Evinger et al., 1991).
However, blinks produce about 5–10 times larger artifacts than
post-saccadic eyelid movements. In frontal electrodes, that is
about 100 μV for blinks as compared to approximately 20μV
for eyelid artifacts succeeding large upward movements when the
recorded data is nose-referenced.
In summary our results confirm that the propagation of eyelid
related artifacts onto the scalp is independent of corneo-retinal
dipole orientation while their amplitude and latency may change
significantly depending on the type of eye and eyelid movement.
Spike potential
The saccadic spike potential is a transient high amplitude poten-
tial observed around saccade onset. Its origin has been a matter
of debate, but although some authors argue for the saccadic
spike potential to emerge from cortical sources (Kur tzberg an d
Vaughan, 1982; Nativ et al., 1990; Parks and Corballis, 2008), it
is now generally believed that saccadic spike potentials reflect the
recruitment of motor units in extra-ocular muscles (Thickbroom
and Mastaglia, 1985, 1986; Picton et al., 2000; Keren et al., 2010;
Carl et al., 2011). The onset of the saccadic spike potential pre-
cedes the onset of the saccade about 5ms and therefore this
artifact is sometimes referred to as “pre-saccadic” spike potential
(Thickbroom and Mastaglia, 1985). However, it has been argued
that saccadic spike potentials and saccades actually start at exact
thesametimebutsincethelatterareusuallydefinedastheeye
movement exceeding a certain velocity threshold, their detection
lags behind the actual movement onset (Keren et al., 2010).
Both, amplitude and topography of the saccadic spike poten-
tial at scalp level change depending on movement size and direc-
tion. Yet, the results in the present study suggest that the observed
topographic patterns might reflect a mixture of corneo-retinal
dipole orientation and different sequences of muscle activation.
More specifically we observed that both, amplitude and topog-
raphy of the saccadic spike potential, are dependent on initial
eye position rather than on saccade size. This may indicate that
at saccade onset the impact of the corneo-retinal dipole on the
overall polarity on the scalp contributes more to the observed
topographic pattern than the saccadic spike potential itself.
It appears virtually impossible to study the saccadic spike
potential’s contribution to the measured signal independently of
other artifact sources, because—as it was shown here and in pre-
vious studies (Keren et al., 2010)—ICA often fails to single out
the saccadic spike potential into one or several separate compo-
nents. One of the reasons, next to its short duration, may be that
different ocular muscle units are recognized as different sources,
which however are too weak to be isolated into independent com-
ponents. Moreover, the problem of studying the properties of the
saccadic spike potential during different types of eye movements
in isolation of other artifacts will also not be easily resolved by
using other methods such as source localization, since their spa-
tial resolution is limited and the precise origin of the mechanisms
generating the spike potential is not well understood yet.
Microsaccades
Microsaccades are performed involuntarily and their execution is
controlled by the superior colliculus (Hafed et al., 2009). Their
behavioral purpose is not entirely clear yet but several studies
suggest that they play an important role for counteracting visual
fading (Martinez-Conde et al., 2004) and enhancing fine spatial
detail (Rucci et al., 2007).However,itremainsanopenquestion
how these putative functions of microsaccades can be brought
in accordance with earlier findings that microsaccades are some-
times suppressed in high-acuity tasks like threading a needle or
shooting a rifle (Winterson and Collewijn, 1976; Bridgeman and
Palca, 1980).
As pointed out above, the most prominent microsaccade-
related confounds in EEG data are produced by the spike potential
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Plöchl et al. Characterization and correction of eye artifacts
at microsaccade onset, while offsets that go along with corneo-
retinal dipole rotation only have a minor impact on the signal.
Consistent with earlier reports (Keren et al., 2010), our results
show that ICA-correction significantly reduces the amplitude of
microsaccade-related spike potentials. Moreover, the finding that
during fixation trials the correction procedure leads to a signal
reduction that follows the observed microsaccade distribution
suggests that microsaccade-related spike potentials do not only
affect the induced gamma band response in the frequency domain
but also ERP amplitudes. Thus, addressing the question which
factors systematically modulate the rate of microsaccades will not
only shed light on the behavioral purpose of microsaccades but
may possibly also reveal spike potential induced confounds in
previous ERP studies.
In summary, the different types of eye artifacts investigated
here are largely independent of each other and display differ-
ent properties with regard to different types of eye movements:
amplitude and topography of corneo-retinal dipole-related arti-
facts are determined by both, eye movement size and direction.
Blink-related artifacts on the other hand are independent of eye
movements and although small eye movements do occur dur-
ing blinking, their impact on the signal is minor as compared
to the eyelid induced resistance change. As a consequence the
only factor significantly modulating the amplitude of blink arti-
facts is whether a blink is performed voluntarily or involuntarily.
In this respect blink artifacts differ from post-saccadic eyelid
artifacts, which do depend on both, eye movement size and
direction. More specifically, at scalp level they are only observed
during upward eye movements a nd display larger amplitudes after
large saccades. However, apart from differences in amplitude all
eyelid-induced artifacts produce the same topographic pattern.
Similarly, the topography of the saccadic spike potential changes
relatively little for different saccade amplitudes and directions and
the observed differences in amplitude seem to depend on the ini-
tial eye position rather than on the type of movement. Apart from
the saccadic spike potential, the contribution of ocular muscle
activity to eye and eyelid movement-related artifacts is negligible
(Mowrer et al., 1935). This holds for both, tonic muscle activity
thatkeepstheeyeoreyelidinagivenpositionandphasicmuscle
activity during blinking.
CORRECTION OF EYE MOVEMENT ARTIFACTS
Regression-based correction
It is widely recognized that blink and saccade-related artifacts
propagate differently onto the scalp (Corby and Kopell, 1972;
Gratton et al., 1983; Antervo et al., 1985; Elbert et al., 1985; Berg
and Scherg, 1991; Picton et al., 2000; Croft et al., 2005). Still, some
authors advocate that both types of artifacts can be fully corrected
by regression methods that are based on only two orthogonal
(i.e., horizontal and vertical) EOG measures, thus ignoring the
independence of eyelid and corneo-retinal dipole-related arti-
facts and the topographic differences between them (Verleger
et al., 1982; Schlögl et al., 2007). Other authors that acknowl-
edge these differences argue that in principle artifacts produced
by eyelid movement during blinking and upward saccades could
be separated from the EOG by including a radial component in
the corneo-retinal dipole model (Elbert et al., 1985; Croft and
Barry, 2000a,b; Schlögl et al., 2007). However, our results demon-
strate that a three-component model does not necessarily result
in better correction performance than a two-component model.
Therefore it appears doubtful that the effects of eyelid movement
on the corneo-retinal dipole can be fully modeled by a single
dipole in three dimensions. It has been shown that a correct mod-
eling of both, vertical eye movements and blinks requires at least
two current dipoles (Antervo et al., 1985). Also, the best dipole
fits for eye movement artifacts are obtained when dipoles are
allowed to take different locations and orientations depending
on the type of movement. More specifically, dipoles that cor-
respond to blink-related activity should not only be located in
front of the vertical eye movement dipole (as it could be mod-
eled by an axial component) but also above it (Berg and Scherg,
1991; Lins et al., 1993a). In addition, it is not clear how to esti-
mate an axial spatial component for a single corneo-retinal dipole
source most adequately, as it may depend on electrodes at many
different locations (Nunez and Srinivasan, 2006), which are not
necessarily symmetric and could contain contributions from neu-
ral sources. In the present study we re-referenced channels to
mathematically linked temporal channels for calculating the axial
component (Elbert et al., 1985; Croft and Barry, 2000a,b). This
however resulted in suboptimal correction coefficients for both,
blinks and eye movements.
Another problem of regression-based methods is that the
regression coefficients are affected by other sources of correla-
tion between EEG channels and EOG channels, principally by
brain sources that propagate to both groups of channels and by
other electrical artifacts like coherent direct-current shifts (Croft
and Barry, 1998a,b). Such correlations could “inflate” or “deflate”
regression coefficients in a manner that depends on both, the size
of the artifacts and the ratio between confounded and artifact-free
periods in the data. For example, regression coefficients calcu-
lated with small saccade amplitudes will be inflated with respect
to those obtained from larger saccades (Croft and Barry, 1998a,b).
To overpass the problem of inflation it has been proposed to sub-
tract event-related activity from the raw data before calculating
the coefficients and in this way to eliminate inflation caused by
the forward propagation of time-locked neural signals into the
EOG channels (Gratton et al., 1983). An alternative proposal sug-
gests that before the calculation of the regression weights, the data
should be averaged with respect to artifact events instead of neu-
ral events, thereby reducing inflation that is produced by brain
activity that is not time-locked to the ocular movement (Croft
and Barry, 1998a,b). Although these methods can help to reduce
the correlation between EEG and EOG channels, they are not
able to completely decorrelate both types of signals. As a conse-
quence inflation of regression coefficients cannot be completely
avoided and activity from neural sources may be subtracted from
the signal.
To summarize, regression initially appears to provide a
straightforward solution for removing eye movement artifacts
from the EEG. But as our results show, it is not possible to cor-
rect corneo-retinal dipole and eyelid artifacts at the same time.
Additionally potential correlations between EEG and regression
channels may lead to inflation of regression coefficients and
erroneous removal of brain activity from the signal. Note that
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Plöchl et al. Characterization and correction of eye artifacts
choosing more advanced regression methods or different regres-
sion channels may have yielded better results as the ones pre-
sented here. However, the general problems of regression-based
eye artifact correction remain and therefore inevitably lead to
suboptimal correction performance.
Independent component analysis correction
ICA does in principle not suffer from the limitations that are
encountered with regression methods. ICA decomposes the signal
measured at scalp level into the activity of the particular sources
that contribute to the signal. Thus, in the ideal case, brain and
artifact-related activity are clearly separated and can be handled
independently. In practice however there are several problems
related to this approach.
First, the effectiveness of ICA strongly depends on the quality
of the signal decomposition. Not all signal sources may be iso-
lated into separate components and there are no definite means to
evaluate whether or not contributions of other sources confound
a particular component. However, with optimally prepared data
(i.e., sufficient length, cleaned from large amplitude noise and
rare artifacts and possibly filtered to the frequency range of inter-
est) this problem can be minimized (Makeig and Onton, 2009). A
large number of studies has shown that ICA is able to isolate all
relevant signal sources in a way that the components of interest,
as for example brain activity from a defined area and/or related
to a certain cognitive task, are not affected by other components,
like artifacts or other cortical sources (Makeig et al., 1996; Makeig
and Onton, 2009; Gramann et al., 2010; Gwin et al., 2010).
A second problem is how to objectively identify components
related to ocular artifacts. Often this is done based on visual
inspection of the components’ topography (Jung et al., 2000;
Iriarte et al., 2003; Makeig and Onton, 2009), thus relying on the
subjective judgment of the experimenter. Here we have shown,
that this approach, not only leads to suboptimal detection and
possible misclassification of ICs, but also to divergent results
depending on the subjective view of the researcher. Likewise,
even more objective methods, like defining ocular artifacts based
on their statistical properties may fail to identify all relevant
components, as different artifacts display very different features
with respect to their propagation pattern, amplitude or frequency
range (Makeig and Onton, 2009). Another conventional method
to identify ocular components is to cluster ICs based on their
sourcelocationsanddefiningthesignalsemergingfromclusters
located in and around the eye and eyelid as eye movement-related
(Gramann et al., 2010; Gwin et al., 2010). However, source clus-
tering might fail in some cases. For example, noisy components
often contain eye artifacts but do not display bipolar patterns and
thus are likely to be mislocalized.
Here we proposed a procedure to identify eye artifact-related
ICs, by comparing their activations during saccade and during
fixation intervals, as defined by high temporal resolution eye
tracking. In this way we circumvent the problem of how to objec-
tively distinguish artifactual from non-artifactual ICs to a large
extent, as this approach does not rely on subjective interpreta-
tion of topographies or prior assumptions about artifact prop-
erties, such as source location or spectral content. Consequently,
the procedure potentially also identifies confounds that are not
directly produced by ocular sources but by artifacts that may
occur during eye movements in a systematic manner, as for
example head or face movement-related muscle artifacts.
We were able to demonstrate that eye movement-related ICs
are almost completely separable from other ICs only based on
their saccade/fixation variance ratio. The selection algorithm pre-
sented here performed more effectively and reliable than human
experts, when those had to base their decision on topographies
alone or in combination with only a small subset of compo-
nent activations. The experts outperformed the automated selec-
tion procedure when they could assess ICs based on both, their
topographies and a large amount of trials. However, given the
number of ICs and trials (here: 896 ICs with an average number of
283 trials each) this approach may not be feasible for most exper-
iments. Employing automated IC classification therefore could
prove as an efficient alternative, especially since the possibly mis-
classified ICs in our experiment were most likely related to noise
rather than to brain activity.
There are, however, potential drawbacks to the proposed
approach: most importantly, brain sources that are mainly active
during the saccade interval might be erroneously excluded. But as
motor planning and sensory processing usually take place before
and after the saccade itself, the exclusion of brain-related compo-
nents is rather unlikely.In the data presented here, none of the ICs
that were identified as eye movement-related appeared to contain
contributions from neural sources.
It also has to be noted that the proposed approach, namely
classifying ICs according to their activation differences between
saccade and fixation intervals, is not entirely free from prior
assumptions. As discussed above, small eye movements also occur
during fixation and therefore might bias the saccade/fixation
variance ratio. However, microsaccades generally occur only in a
subset of fixation intervals, and are even less frequent when eye
movements are allowed during the experiment. Additionally, spike
potentials, which have been identified as the main microsaccade-
related confound in EEG data (Yuval-Greenberg et al., 2008; Keren
et al., 2010), are not likely to contribute as much to the variance
of generally longer fixation periods as to the usually much shorter
saccade intervals. Beside these assumptions, the ratio-threshold
defining whether a given IC is eye movement-related or not, was
set based on heuristics. For our data the pre-defined value of 1.1
proved to be optimally chosen. In other experiments, however,
the discrimination threshold may have to be adapted. On the
other hand here all ICs with ratios lower than 1.1 but above
about 0.9 appeared to be exclusively related to muscle activity
and noise, respectively. Therefore we conclude that even setting
the threshold to 1 is not likely to affect task relevant neural signals.
Another limitation is that the IC selection procedure we sug-
gested here requires high temporal resolution eye tracking, which
may not always be available in standard EEG experiments. It may
however be possible to extract saccade periods based on typical
eye movement signatures in the EOG channels, such as signal
deflections and/or the saccadic spike potential (Keren et al., 2010).
Lastly, effective eye artifact correction using ICA does not
only depend on a sufficient amount of eye movements in the
data but also on their amplitude. Applying the procedure on
another data set containing fewer and smaller saccades resulted
Frontiers in Human Neuroscience www.frontiersin.org October 2012 | Volume 6 | Article 278 |20

Plöchl et al. Characterization and correction of eye artifacts
in a suboptimal ICA decomposition. Therefore it is highly recom-
mended to include a variety of eye movements that are performed
independently from the task at hand in the ICA decomposition,
as we did in our pre-experimental procedure.
Neural activity accompanying eye movements
Studying brain activity in the presence of eye movements entails
another, often overlooked aspect that cannot be solved by arti-
fact correction: the contribution of eye- and eyelid movements to
the signal measured on the scalp does not only consist of non-
cortical sources. They are also accompanied by cortical activity
related to motor preparation, perceptual suppression, and sensory
responses. As blinks and eye movements are also present in exper-
imental designs that require fixation, their neural concomitants
are easily overlooked especially when relying on artifact correc-
tion procedures. Thus, in the presence of voluntary or involuntary
eye- and eyelid movements, comparisons between experimental
conditions may be influenced by systematic variation of brain
activity related to eye movement execution, attentional control
(Glimcher, 2003; Müri, 2006; Corbetta et al., 2008), visual sup-
pression and stability during the saccade (Wurtz et al., 2008)or
the related visual responses (Dimigen et al., 2009, 2011; Ossandon
et al., 2010). Neglecting these potentially systematic neural sig-
nals may lead to an erroneous interpretation of the data. Similar
to saccades, blinks and eyelid movements are also partially con-
trolled by cortical structures. In monkeys several frontal and
parietal areas have been linked to the control of blinks (Gong
et al., 2005). In humans, activity in the medial frontal gyrus (Yoon
et al., 2005) has been related to spontaneous blinking and activity
in precentral motor areas to voluntary blinking or the active inhi-
bition of it (Kato and Miyauchi, 2003; Yoon et al., 2005; Chung
et al., 2006). Furthermore blinking has also an impact on sensory
processing, as it causes suppression of activity in visual, pari-
etal and prefrontal cortices (Bristow et al., 2005), thereby leading
to a decrease of visual sensitivity around the blinking period
(Volkmann et al., 1980). Additionally the interruption of visual
input while the eyelid covers the pupil, a period that may range
from 100 to 300 ms, usually goes unnoticed. This phenomenon,
commonly termed as “visual continuity,” has been related to
activity in parieto-occipital areas during blinking (Bristow et al.,
2005). Although not consciously processed, blink-related visual
potentials do display differences depending on the degree of
illumination (Berg and Davies, 1988).
Lastly, dueto the occurrence ofmicrosaccades, eye movement-
related brain signals are likely to be present even during fixa-
tion intervals. Dimigen et al. (2009) have recently shown that
microsaccades are not only accompanied by extra-cortical con-
founds (i.e., spike potentials) but also by activity emerging from
cortical sources. They found that microsaccades generate visually
evoked potentials, so called lambda responses, as they typically
occur in response to stimulus onset or after larger voluntary
saccades (Dimigen et al., 2009).
Altogether these findings imply that eye movements influence
the recorded EEG in a way that cannot be separated from neu-
ronal processing. Therefore experimenters should be aware that
frequency and size of eye and eyelid movements may vary system-
atically between conditions: saccade rate for instance depends on
a variety of aspects like attention, task and image features. Also,
the probability of fixational eye movements is known to change
in dependency of behavioral task (Winterson and Collewijn,
1976; Bridgeman and Palca, 1980), proportion of target stim-
uli in oddball paradigms (Dimigen et al., 2009)andimagetype
(Rucci et al., 2007; Yuval-Greenberg et al., 2008). Similarly, size,
frequency, and timing of blinks were found to depend on dif-
ferent cognitive and experimental factors such as attentional
breaks (Siegle et al., 2008; Nakano and Kitazawa, 2010), mind-
wandering (Smilek et al., 2010), use of startle stimuli (Lang
et al., 1990), and the occurrence of certain saccade types as for
instance while changing lines during reading (Orchard and Stern,
1991).
In summary, relying on artifact correction methods alone
would mean to ignore the fact that eye movements do not only
introduce artifacts to the EEG but that they also go along with
neural activity, which when overlooked may lead to misinter-
pretation of the data. But eye movements are an essential part
of human cognition and experimental setups where they have
to be suppressed may not provide adequate information about
neural processing under natural conditions. Moreover, system-
atic variations of eye movements between conditions directly
result from differences in cognitive processing and therefore their
respective neural signatures cannot be dismissed as “confounds”.
In many cases, especially when studying overt visual attention,
these alleged “neural confounds” constitute exactly the activity of
interest. Thus, eye movements during EEG recordings should not
necessarily be considered as interferences but as a part of natural
human behavior. Their presence, however, demands the experi-
menter’s awareness and the observed patterns of neural activity
have to be interpreted carefully in the sense that they may be
linked to visual attention and saccade execution rather than to
other cognitive processes that may be the actual focus of interest.
Under this point of view simultaneous eye tracking or other sac-
cade detection methods, may help to identify possible biases that
arise from systematic differences in probability, size, and timing
of eye- and eyelid movements (cf. Kierkels et al., 2007; Yuval-
Greenberg et al., 2008; Keren et al., 2010; Dimigen et al., 2011).
CONCLUSION
A large number of studies have investigated individual eye move-
ment artifacts under various aspects (e.g., Thickbroom and
Mastaglia, 1985; Chioran and Yee, 1991; Lins et al., 1993a,b;
Helmchen and Rambold, 2007; Keren et al., 2010)andavari-
ety of different methods has been proposed to correct or reduce
their impact on the EEG (e.g., Lins et al., 1993a; Croft and Barry,
2000a,b; Jung et al., 2000; Iriarte et al., 2003; Schlögl et al., 2007).
There are efforts to optimize these methods in order to overcome
some of their inherent limitations (e.g., Gratton et al., 1983; Croft
and Barry, 1998a,b; Makeig and Onton, 2009; Keren et al., 2010)
and it has been suggested to complement EEG measurements
with eye-tracker information in order to address the problems
and pitfalls that are connected to recording EEG in the presence
of eye movements (e.g., Kierkels et al., 2007; Dimigen et al., 2011).
Here, by co-registering EEG and eye movements, we stud-
ied a wide range of eye artifacts and reinvestigated a number of
previous findings within one single data set. This made it possible
Frontiers in Human Neuroscience www.frontiersin.org October 2012 | Volume 6 | Article 278 |21