using eye tracker for accurate eye movement artifact correction

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    Using an Eye Tracker for Accurate Eye MovementArtifact Correction

    Joep J. M. Kierkels*, Jamal Riani, Jan W. M. Bergmans, Senior Member, IEEE, and Geert J. M. van Boxtel

    AbstractWe present a new method to correct eye movementartifacts in electroencephalogram (EEG) data. By using an eyetracker, whose data cannot be corrupted by any electrophysio-logical signals, an accurate method for correction is developed.The eye-tracker data is used in a Kalman filter to estimate whichpart of the EEG is of ocular origin. The main assumptions foroptimal correction are summed and their validity is proven. Theeye-tracker-based correction method is objectively evaluated onsimulated data of four different types of eye movements and visu-ally evaluated on experimental data. Results are compared to threeestablished correction methods: Regression, Principal ComponentAnalysis, and Second-Order Blind Identification. A comparisonof signal to noise ratio after correction by these methods is givenin Table II and shows that our method is consistently superiorto the other three methods, often by a large margin. The useof a reference signal without electrophysiological influences, asprovided by an eye tracker, is essential to achieve optimal eyemovement artifact removal.

    Index TermsArtifact removal, electroencephalography, eyemovements, modeling.


    TO CORRECT the electroencephalogram (EEG) for eyemovement and blink artifacts, many correction methodshave been developed over the past years [1][4]. Especiallyin research areas where the EEG signals of interest have verylow amplitudes and are of short duration, as for single-trialexperiments, it is important that the correction method removesas much of the artifact as possible. Often, like in habituationstudies or in studies involving children or ADHD subjects, itis not possible or undesired to repeat the experiment numeroustimes if artifacts occur. Furthermore, the electrical activity ofbrain processes that mainly occur in the frontal lobe is difficultto detect because frontal electrode positions can contain eyemovement artifacts of large amplitude.

    Both brain activity and eye movements cause electric cur-rents through the brain. Therefore, the recorded EEG signal isa combination of ocular and brain-related components. Aftera recorded signal is corrected for ocular artifacts (OAs) it is

    Manuscript received January 1, 2006; revised October 29, 2006. This workwas supported by a grant from the Co-operation Centre Tilburg and EindhovenUniversities. Asterisk indicates corresponding author.

    *J. J. M. Kierkels is with the Electrical Engineering Department, EindhovenUniversity of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Nether-lands (e-mail:

    J. Riani and J. W. M. Bergmans are with the Electrical Engineering Depart-ment, Eindhoven University of Technology, 5600 MB, Eindhoven, The Nether-lands.

    G. J. M. van Boxtel is with the Psychology Department, University of Tilburg,5000 LE, Tilburg, The Netherlands.

    Digital Object Identifier 10.1109/TBME.2006.889179

    difficult to judge if, and to what extent, correction was suc-cessful because the brain and ocular components are not sep-arately known. For this reason, it is also not yet possible to ob-jectively determine the quality of existing correction methods,and hence their adequacy for challenging applications like thosementioned above.

    In order to develop a standard against which existing methodscan be compared, it is necessary to have a method that, in prin-ciple, can achieve optimal correction. The goal of this study isto develop such a method and use it to objectively determine thequality of correction of existing methods.

    All existing correction methods are, to our knowledge, purelybased on electrical potential recordings. If either the ocular orthe brain component in the EEG can be reconstructed withoutthe other, it is possible to extract both components from themixture and objectively determine the quality and adequacy ofcorrection methods. The ocular component is caused by a dif-ference in potential between the front and the back of the eye,known as the corneo-retinal dipole [5]. Eye movements changethe orientation of this dipole and thus, via volume conductionthrough the head, also change the magnitude of the ocular com-ponent. Eye blinks and smaller eyelid movements also causechanges in potential at the electrode positions, which can resultin artifacts with amplitudes of up to 300 . The origin of thechange in potential as caused by blinks is different from eyemovement potential changes. Blinks briefly change the shapeof the volume that surrounds the corneo-retinal dipole. As aresult, the attenuation of blink artifacts from frontal to occip-ital electrodes is different from the attenuation of eye move-ment artifacts. Moreover, the specific influences of eye move-ments or eyelid movement on the EEG are difficult to discern.Many studies have demonstrated that there is an accompanyingeye movement during a blink and, similarly, during most eyemovements there is an accompanying eyelid movement [6][8].Modeling these two artifacts requires two different approaches.In this paper, the focus is on both simulated and recorded eyemovement artifacts. By omitting the effects of blinks and eyelidmovement during eye movements in our simulations, a consid-erable simplification is made. A correction method that claimsto correct for both blinks and for eye movements should, how-ever, be able to correct the data presented here as well, becausethe eyelid position is fixed in our simulations.

    We propose to record the orientation of the eye by an eyetracker in order to provide information on the OA that doesnot contain any cerebral component. As a measure that repre-sents the orientation of the dipole, the eye-tracker records thehorizontal and vertical position of the pupil, denoted byand , respectively. These positions, combined in a vector

    , are indicative of the ocular orientation.

    0018-9294/$25.00 2007 IEEE


    Fig. 1. Use of an eye tracker as a basis for eye movement artifact correction.The enclosed shape in this figure represent the human part of the setup. Solidarrows indicate potentials.

    In Fig. 1, it is illustrated that a recorded EEG, , containspotentials of both cerebral, , and ocular origins, with

    for eye. The potential is determined by the ocular ori-entation and by the conductive properties of the head and is as-sumed to be a function of . The separation of the compo-nents in is illustrated in the lower part of Fig. 1. Becausechanges in have instantaneous effects on electric potential,due to volume conduction through the head [9], vector canbe converted to an estimate of , denoted by . Forthis conversion, it is necessary that the conductive properties ofthe head are parameterized in a way that allows for the calcula-tion of based on the vector . By subtractingfrom , an estimate for the cerebral component can also beobtained, denoted as .

    The relation between and is unknown and de-pends, among other things, on physical properties of the subject,like the diameter of the head and exact morphology of the skull,brain and other biological tissues. Obviously, the relation alsodepends on nonsubject-related properties, like electrode place-ment and the luminance over the retina.

    In this paper, it is assumed, and verified, that this relationcan be parameterized by using first- and second-order combina-tions of . The resulting parameters, combined in a parametervector , are a priori unknown as they represent the physical-and nonsubject-related properties discussed earlier, and must beestimated based on recorded data. For accurate artifact removal,it is essential that this estimation is accurate.

    Traditionally, artifact correction also combined several un-known elements in one parameter or a vector of parame-ters. The main difference with the current approach is thatonly focuses on the relation between pupil position and recordedEEG.

    The vector is usually estimated nonadaptively, eitherduring a calibration session, or directly on the data of interest,and leading to estimate vector . Nonadaptive methods esti-mate a constant , over a period of time. Fluctuations ofin time will result in sub-optimal correction as a fluctuation ofonly one percent can cause new artifacts of several .

    This can be overcome in two ways. Firstly, the length of therecording can be reduced to decrease fluctuations of withinthis recording. Examples of such are recordings during which

    is recalibrated at fixed times, or component analysis over anepoch of only a few seconds. The effects of a shorter epoch onaccuracy of regression-based and component-based methods isstudied, e.g., in [10].

    In this study, it was found that small parameter fluctuationsare less problematic for regression-based methods. Furthermorecorrection over a 60 s epoch was significantly worse than cor-rection over a 1 s epoch, which supports the idea that doesfluctuate. A difficulty with this approach is that after correctionthe epochs need to be re-attached and jumps may occur. Sec-ondly, by using parameter adaptation to track , it is pos-sible to adapt to parameter fluctuations and have an accurate

    throughout a recording of any duration. This results in asmooth corrected signal that does not suffer from re-attachingproblems. Given these advantages, adaptive parameter estima-tion is used in this study.

    Vector is, thus, obtained adaptively by a feedback-loopin which is used as a basis for adaptation. Adaptation isindicated in Fig. 1 by the dashed gray arrow. The motivationbehind the use of at most second-order parameterization


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