Extended ICA Removes Artifacts from 
Electroencephalographic Recordings 
Tzyy-Ping Jung , Colin Humphries , Te-Won Lee , Scott Makeig 2'3, 
Martin J. McKeown , Vicente Iragui 3, Terrence J. Sejnowski  
Howard Hughes Medical Institute and Computational Neurobiology Lab 
The Salk Institute, P.O. Box 85800, San Diego, CA 92186-5800 
 j uag, colin, t ewon, SCOtt ,martin, t errysalk. edu 
2Naval Health Research Center, P.O. Box 85122, San Diego, CA 92186-5122 
SDepartment of Neurosciences, University of California San Diego, La Jolla, CA 92093 
Abstract 
Severe contamination of electroencephalographic (EEG) activity 
by eye movements, blinks, muscle, heart and line noise is a serious 
problem for EEG interpretation and analysis. Rejecting contami- 
nated EEG segments results in a considerable loss of information 
and may be impractical for clinical data. Many methods have been 
proposed to remove eye movement and blink artifacts from EEG 
recordings. Often regression in the time or frequency domain is 
performed on simultaneous EEG and electrooculographic (EOG) 
recordings to derive parameters characterizing the appearance and 
spread of EOG artifacts in the EEG channels. However, EOG 
records also contain brain signals [1, 2], so regressing out EOG ac- 
tivity inevitably involves subtracting a portion of the relevant EEG 
signal from each recording as well. Regression cannot be used to 
remove muscle noise or line noise, since these have no reference 
channels. Here, we propose a new and generally applicable method 
for removing a wide variety of artifacts from EEG records. The 
method is based on an extended version of a previous Indepen- 
dent Component Analysis (ICA) algorithm [3, 4] for performing 
blind source separation on linear mixtures of independent source 
signals with either sub-Gaussian or super-Gaussian distributions. 
Our results show that ICA can effectively detect, separate and re- 
move activity in EEG records from a wide variety of artifactual 
sources, with results comparing favorably to those obtained using 
regression-based methods. 
Extended ICA Removes Artifacts from EEG Recordings 895 
1 Introduction 
Eye movements, muscle noise, heart signals, and line noise often produce large and 
distracting artifacts in EEG recordings. Rejecting EEG segments with artifacts 
larger than an arbitrarily preset value is the most commonly used method for elim- 
inating artifacts. However, when limited data are available, or blinks and muscle 
movements occur too frequently, as in some patient groups, the amount of data 
lost to artifact rejection may be unacceptable. Methods are needed for removing 
artifacts while preserving the essential EEG signals. 
Berg & Scherg [5] have proposed a spatio-temporal dipole model for eye-artifact re- 
moval that requires a priori assumptions about the number of dipoles for saccade, 
blink, and other eye-movements, and assumes they have a simple dipolaf structure. 
Several other proposed methods for removing eye-movement artifacts are based on 
regression in the time domain [6, 7] or frequency domain [8, 9]. However, simple 
time-domain regression tends to overcompensate for blink artifacts and may intro- 
duce new artifacts into EEG records [10]. The cause of this overcompensation is 
the difference between the spatial EOG-to-EEG transfer functions for blinks and 
saccades. Saccade artifacts arise from changes in orientation of the retinocorneal 
dipole, while blink artifacts arise from alterations in ocular conductance produced 
by contact of the eyelid with the cornea [11]. The transfer of blink artifacts to 
the recording electrodes decreases rapidly with distance from the eyes, while the 
transfer of saccade artifacts decreases more slowly, so that at the vertex the effect 
of saccades on the EEG is about double that of blinks [11], while at frontal sites 
the two effects may be near-equal. 
Regression in the frequency domain [8, 9] can account for frequency-dependent 
spatial transfer function differences from EOG to EEG, but is acausal and thus 
unsuitable for real-time applications. Both time and frequency domain regression 
methods depend on having a good regressor (e.g., an EOG), and share an inherent 
weakness that spread of excitation from eye movements and EEG signals is bidirec- 
tional. This means that whenever regression-based artifact removal is performed, a 
portion of relevant EEG signals also contained in the EOG data will be cancelled out 
along with the eye movement artifacts. Further, since the spatial transfer functions 
for various EEG phenomena present in the EOG differ from the regression transfer 
function, their spatial distributions after artifact removal may differ from the raw 
record. Similar problems complicate removal of other types of EEG artifacts. Rel- 
atively little work has been done on removing muscle activity, cardiac signals and 
electrode noise from EEG data. Regressing out muscle noise is impractical since 
regressing out signals from multiple muscle groups require multiple reference chan- 
nels. Line noise is most commonly filtered out in the frequency domain. However, 
current interest in EEG in the 40-80 Hz gamma band phenomena may make this 
approach undesirable as well. 
We present here a new and generally applicable method for isolating and removing 
a wide variety of EEG artifacts by linear decomposition using a new Independent 
Component Analysis (ICA) algorithm [4] related to a previous algorithm [3, 12]. 
The ICA method is based on spatial filtering and does not rely on having a 
"clean"reference channel. It effectively decomposes multiple-channel EEG data 
into spatially-fixed and temporally independent components. Clean EEG signals 
can then be derived by eliminating the contributions of artifactual sources, since 
their time courses are generally temporally independent from and differently dis- 
tributed than sources of EEG activity. 
896 T-P Jung, C. Humphries, T-W. Lee, S. Makeig, M. J. McKeown, V. Iragui and T. J. Sejnowski 
Independent Component Analysis 
Bell and Sejnowski [3] have proposed a simple neural network algorithm that blindly 
separates mixtures, x, of independent sources, s, using infomax. They show that 
maximizing the joint entropy, H(y), of the output of a neural processor minimizes 
the mutual information among the output components, Yi = g(ui), where g(ui) is 
an invertible bounded nonlinearity and u = Wx. This implies that the distribution 
of the output yi approximates a uniform density. Independence is achieved through 
the nonlinear squashing function which provides necessary higher-order statistics 
through its Taylor series expansion. The learning rule can be derived by maximizing 
output joint entropy, H(y), with respect to W [3], giving, 
0H(Y) wTw ---- [I + pu T] W (1) 
AYV x 0W  
where i6i -- (O/Oui)ln(Oyi/Oui). The 'natural gradient' WTW term [13] avoids 
matrix inversions and speeds convergence. The form of the nonlinearity g(u) plays 
an essential role in the success of the algorithm. The ideal form for g() is the cu- 
mulative density function (cdf) of the distributions of the independent sources. In 
practice, if we choose g() to be a sigmoid function (as in [3]), the algorithm is then 
limited to separating sources with super-Gaussian distributions. An elegant way of 
generalizing the learning rule to sources with either sub- or super-Gaussian distri- 
butions is to approximate the estimated probability density function (pdf) in the 
form of a 4th-order Edgeworth approximation as derived by Girolami and Fyfe [14]. 
For sub-Gaussians, the following approximation is possible: 
For super-Gaussians, the same approximation becomes/5i = - tanh(ui) - ui. The 
sign can be chosen for each component using its normalized kurtosis, k4(ui), giving, 
AW x 0H(Y)wTw = [I - sign(k4)tanh(u)u T - uu T] W (2) 
ow 
Intuitively, for super-Gaussians the -tanh(u)u T term is an anti-Hebbian rule that 
tends to minimize the variance of u, whereas for sub-Gaussians the corresponding 
term is a Hebbian rule that tends to maximize its variance. 
2.1 Applying ICA to artifact correction 
The ICA algorithm is effective in performing source separation in domains where, 
(1) the mixing medium is linear and propagation delays are negligible, (2) the time 
courses of the sources are independent, and (3) the number of sources is the same 
as the number of sensors, meaning if we employ N sensors the ICA algorithm can 
separate N sources [3, 4, 12]. In the case of EEG signals [12], volume conduc- 
tion is thought to be linear and instantaneous, hence assumption (1) is satisfied. 
Assumption (2) is also reasonable because the sources of eye and muscle activity, 
line noise, and cardiac signals are not generally time locked to the sources of EEG 
activity which is thought to reflect activity of cortical neurons. Assumption (3) is 
questionable since we do not know the effective number of statistically-independent 
signals contributing to the scalp EEG. However, numerical simulations have con- 
firmed that the ICA algorithm can accurately identify the time courses of activation 
and the scalp topographies of relatively large and temporally-independent sources 
from simulated scalp recordings, even in the presence of a large number of low-level 
and temporally-independent source activities [16]. 
For EEG analysis, the rows of the input matrix x are the EEG signals recorded at 
different electrodes, the rows of the output data matrix u -- Wx are time courses of 
activation of the ICA components, and the columns of the inverse matrix, W-, give 
the projection strengths of the respective components onto the scalp sensors. The 
Extended ICA Removes Artifacts from EEG Recordings 897 
scalp topographies of the components provide evidence for their biological origin 
(e.g., eye activity should project mainly to frontal sites). In general, and unlike 
PCA, the component time courses of activation will be nonorthogonal. 'Corrected' 
EEG signals can then be derived as x t - (W)-u t, where u t is the matrix of 
activation waveforms, u, with rows representing artifactual sources set to zero. 
3 Methods and Materials 
One EEG data set used in the analysis was collected from 20 scalp electrodes placed 
according to the International 10-20 System and from 2 EOG placements, all re- 
ferred to the left mastoid. A second EEG data set contained 19 EEG channels (no 
EOG channel). Data were recorded with a sampling rate of 256 Hz. ICA decompo- 
sition was performed on 10-sec EEG epochs from each data set using Matlab 4.2c on 
a DEC 2100A 5/300 processor. The learning batch size was 90, and initial learning 
rate was 0.001. Learning rate was gradually reduced to 5 x 10 -e during 80 training 
iterations requiring 6.6 min of computer time. To evaluate the relative effective- 
ness of ICA for artifact removal, the multiple-lag regression method of Kenemans 
et al. [17] was performed on the same data. 
4 Results 
4.1 Eye movement artifacts 
Figure i shows a 3-sec portion of the recorded EEG time series and its ICA com- 
ponent activations, the scalp topographies of four selected components, and the 
'corrected' EEG signals obtained by removing four selected EOG and muscle noise 
components from the data. The eye movement artifact at 1.8 sec in the EEG data 
(left) is isolated to ICA components 1 and 2 (left middle). The scalp maps (right 
middle) indicate that these two components account for the spread of EOG activity 
to frontal sites. After eliminating these two components and projecting the remain- 
ing components onto the scalp channels, the 'corrected' EEG data (right) are free 
of these artifacts. 
Removing EOG activity from frontal channels reveals alpha activity near 8 Hz that 
occurred during the eye movement but was obscured by the eye movement artifact 
in the original EEG traces. Close inspection of the EEG records (Fig. lb) confirms 
its presence in the raw data. ICA also reveals the EEG 'contamination' appearing 
in the EOG electrodes (right). By contrast, the 'corrected' EEG resulting from 
multiple-lag regression on this data shows no sign of 8 Hz activity at Fpl (Fig. 
lb). Here, regression was performed only when the artifact was detected (1-sec 
surrounding the EOG peak), since otherwise a large amount of EEG activity would 
also have been regressed out during periods without eye movements. 
4.2 Muscle artifacts 
Left and right temporal muscle activity in the data are concentrated in ICA com- 
ponents 14 and 15 (Fig. la, right middle). Removing them from the data (right) 
reveals underlying EEG activity at temporal sites T3 and T4 that had been masked 
by muscle activity in the raw data (left). The signal at T3 (Fig. lc left) sums muscle 
activity from component 14 (center) and underlying EEG activity. Spectral analy- 
sis of the two records (right) shows a large amount of overlap between their power 
spectra, so bandpass filtering cannot separate them. ICA component 13 (Fig. la, 
left middle) reveals the presence of small periodic muscle spiking (in right frontal 
channels, map not shown) that is highly obscured in the original data (left). 
898 T-P Jung, C. Humphries, T- W. Lee, S. Makeig, M. J. McKeown, V. Iragui and T. J. Sejnowski 
Time Course of Scalp Maps of 
(a) Original EEG ICA Com )onents ICA Components 
(b) 
Fpl 
Fp2 
F3 
F4 
C3 
C4 
A2 
P3 
P4 
01 
02 
F7 
F8 
T3 
T4 
T5 
T6 
Fz 
Cz 
Pz 
EOG1 
EOG2 
0 
1 2 3 0 1 2 3 
Time(sec) 
Corrected EEG 
EEG -- Fpluslng 
multiple--I g re ressio 
Removed using ICA 
O I 2 3 4 5 
Time(sec) 
(C) Power Spectral Density 
40 
-- Remaining EEG - T3 
Original EEG - T3   
-..-  .... . -;,14J_.-., 'T  Muscle Artifact - T3 
30 
20 
0 16 32 48 64 
Hz 
Figure 1: A 3-sec portion of an EEG time series (left), corresponding ICA com- 
ponents activations (left middle), scalp maps of four selected components (right 
middle), and EEG signals corrected for artifacts according to: (a) ICA with the 
four selected components removed (right), or (b) multiple-lag regression on the two 
EOG channels. ICA cancels multiple artifacts in all the EEG and EOG channels si- 
multaneously. (c) The EEG record at T3 (left) is the sum of EEG activity recorded 
over the left temporal region and muscle activity occurring near the electrode (cen- 
ter). Below 20 Hz, the spectra of remaining EEG (dashed line) and muscle artifact 
(dotted line) overlap strongly, whereas ICA separates them by spatial filtering. 
Extended ICA Removes Artifacts from EEG Recordings 899 
4.3 Cardiac contamination and line noise 
Figure 2 shows a 5-see portion of a second EEG time series, five ICA components 
that represent artifactual sources, and 'corrected' EEG signals obtained by remov- 
ing these components. Eye blink artifacts at 0.5, 2.0 and 4.7 sec (left) are detected 
and isolated to ICA component 1 (middle left), even though the training data con- 
tains no EOG reference channel. The scalp map of the component captures the 
spread of EOG activity to frontal sites. Component 5 represents horizontal eye 
movements, while component 2 reveals the presence of small periodic muscle spik- 
ing in left frontal channels which is hard to see in the raw data. Line noise has a 
sub-Gaussian distribution and so could not be clearly isolated by earlier versions of 
the algorithm [3, 12]. By contrast, the new algorithm effectively concentrates the 
line noise present in nearly all the channels into ICA component 3. The widespread 
cardiac contamination in the EEG data (left) is concentrated in ICA component 
4. After eliminating these five artifactual components, the 'corrected' EEG data 
(right) are largely free of these artifacts. 
5 Discussion and Conclusions 
ICA appears to be an effective and generally applicable method for removing known 
artifacts from EEG records. There are several advantages of the method: (1) ICA 
is computationally efficient. Although it requires more computation than the algo- 
rithm used in [15, 12], the extended ICA algorithm is effective even on large EEG 
data sets. (2) ICA is generally applicable to removal of a wide variety of EEG arti- 
facts. (3) A simple analysis simultaneously separates both the EEG and its artifacts 
into independent components based on the statistics of the data, without relying 
on the availability of 'clean' reference channels. This avoids the problem of mutual 
contamination between regressing and regressed channels. (4) No arbitrary thresh- 
olds (variable across sessions) are needed to determine when regression should be 
performed. (5) Once the training is complete, artifact-free EEG records can then 
be derived by eliminating the contributions of the artifactual sources. However, 
the results of ICA are meaningful only when the amount of data and number of 
channels are large enough. Future work should determine the minimum data length 
and number of channels needed to remove artifacts of various types. 
Acknowlegement 
This report was supported in part by grants from the Office of Naval Research. The 
views expressed in this article are those of the authors and do not reflect the official 
policy or position of the Department of the Navy, Department of Defense, or the 
U.S. Government. Dr. McKeown is supported by a grant from the Heart &; Stroke 
Foundation of Ontario. 
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