A New Hybrid Method for EOG Artifact Rejection

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Proceedings oE the 9 th In ternat ional C o nE e re nc e o n I n E o r m a t i o n Technology an dAppl icat ions in Biomedic ine , ITAB 2009, Larnaca, cyprus , 5 - 7 November 2009

REG-leA:

ANew Hybrid Method for EOGArtifact Rejection

Manousos A. Klados, Christos L. Papadelis, Panagiotis D. Bamidis

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Raw RegresEEG Reconsion to tructiorData EOGXm[l , 00[1, ..,n] '1',,[1, ..,n]

biological/physiological. The most frequently seenphysiological artifacts are due to ocular, heart or muscularactivity. Ocular artifacts, such as eye blinks, are the mosttroublesome among the biological artifacts.The ocular signal stems from the fact that the cornea ispositively charged with reference to the retina. Thisretinocorneal potential difference generates a dipole withinthe eye - ball, and, therefore, ocular artifacts are in due tothe reorientation of the aforementioned dipole [I] .

Fig, I Block diagram of the REG-ICA approach.The eye - blinking artifacts could also be the result ofalternations in conductance arising from the contact of theeyelid with the cornea [2]. All the signals derived fromocular activity can be measured by placing electrodes nearthe eyes; this type of measurement is calledelectrooculogram (EOG).A variety of algorithms have been proposed for thedetection and correction of EOG artifacts. These algorithmscan be separated into two main classes. The first class iscomposed by regression - based techniques. According tothis methodology, the regression - based methods computethe amplitude relationship between the EEG and EOGchannels in order to define the backward propagationcoefficients and fmally subtract the EOG from the EEGsignals.On the other hand, Blind Source Separation (BSS)methods, like Independent Component Analysis (ICA) havethe ability to separate EEG signals into statisticallyindependent components (lCs); specialists are then called toidentify the artifactual ICs, which are then algorithmicallyremoved; finally, the signal is reconstruct so as to be free of

artifacts [3]. The major disadvantage of BSS - basedmethodologies is that an artifactual component contains alsoneural activity aside from pure artifacts; thus the removal ofthe contaminated ICs leads to the distortion of theunderlying cerebral signals.The current study, tries to deal with the aforementioneddisadvantage by demonstrating the use of the regressionanalysis in order to remove only EOG artifacts from ICskeeping the neural activity intact. A block diagram of theproposed automatic approach is shown in Fig.I . Accordingto this, l eA is applied in raw EEG signals decomposing

Keywords- Artifact Rejection, Independent ComponentAnalysis (ICA), Regression, Electroencephalogram (EEG),Electroculogram (EOG)

Abstract-The plethora of Artifact Rejection (AR)techniques proposed for removing electrooculographic (EOG)artifacts from electroencephalographic (EEG) signals can beseparated into two main categories. The first category iscomposed of regression - based methods, while the second oneconsists of Blind Source Separation (BSS) - methods. A majordisadvantage of BSS- based methodology is that the artifactualcomponents include also neural activity, thus their rejectionleads to the distortion of the underlying cerebral activity. Thecurrent study tries to solve the aforementioned problem byproposing a new hybrid algorithm for EOG AR. According tothis automatic approach, called REG-ICA, IndependentComponent Analysis (ICA) is used to decompose EEG signalsinto spatial independent components (ICs). Then an adaptivefilter, based on a stable Version of the Recursive Least Square(sRLS) algorithm, is applied to ICs so as to remove only EOGartifacts and maintain the neural signals intact. Then thecleaned ICs are projected back, reconstructing the artifact free EEG signals. In order to evaluate the performance of theproposed technique, REG-ICA has been compared with theLeast Mean Square (LMS) approach, in simulated EEG data.Two criteria were used for the comparison: how successfullyalgorithms remove eye blinking artifacts, and how much theEEG signals ar e distorted. Results support the argument thatREG-ICA removes successfully EOG activity, while itminimizes the distortion of the underlying cerebral activity incontrast to LMS.

M.A. Klados is with the Group ofApplied Neuroscience, Labof MedicalInformatics, Medical School, Aristotle University of Thessaloniki,Thessaloniki, CO 54124 Greece (e-mail: [email protected]).C.L. Papadelis is with Center for BrainlMind Sciences (CIMEC),University of Trento, Mattarello, Trentino , Italy (e-mail :[email protected]).P.D. Bamidis is with the Group of Applied Neuroscience, Lab ofMedical Informatics, Medical School, Aristotle University of Thessaloniki,Thessaloniki, CO 54124 Greece (corresponding author to provide phone:30-2310-999310; fax: 30-2310-999263; e-mail: [email protected]).

I. INTRODUCTION

N owadays electroencephalography (EEG) is commonlyused for understanding cerebral functions as much asfor evaluating neuronal abnormalities, brain injuriesand disorders. Artifacts are the outstanding enemy of highquality encephalography and their presence may seriouslyaffect the Event Related Potentials (ERP) analysis or theepileptic seizure spikes. Artifacts originate from two majorsource categories: artificial/technical and

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II. MATERIALS AND METHODS

C. REG-ICA AlgorithmA widely used version of ICA decomposes signals tostatistical independent components using an information

them to statistical independent components; then aregression algorithm is used in order to remove only ocularartifacts from ICs. The filtered ICs are then projected back,reconstructing the artifact - free EEG signals.So the remainder of this paper is structured as follows. Insection II methodological background is provided alongsidea detailed description of the proposed hereinmethod/algorithm. Section III consists of tables andillustrations of the results; the latter are fmally discussed inlast two sections of the paper.

A. Independent Component AnalysisA BSS model assumes that a set of recordings of p

random variables u(t) = [ul(t), ...,Up(t)]T are linear mixturesof q independent source signals. Therefore, the BSS modelwhich describes the aforementioned process of mixing theICs can be described by:

u(t) =A set) + n(t) (1)where A is a [p x q]matrix and n(t) denotes an additivevector ofwhite noise. The least information required for thismodel is that the source signals should be independent. ABSS algorithm fmds the ICs without a priori knowledge ofthe mixing process or the source signals. Furthermore twoadditional assumptions have to be met in order to retrievecorrectly the ICs: the number of source signals has to be atmost equal to the number of recordings and the mixingmatrix has to be full column-rank.

maximization algorithm [4]. According to this approach,maximizing the joint entropy H(y) of the output of a neuralprocessor minimizes the mutual information among the ICs.An extended version of ICA [3], to super - sub Gaussiansignals, was used in the proposed AR technique in order toextract ICs.Our approach lies in the assumption, that the independentcomponents are contaminated by EOG artifacts according tothe linear model described in (2). An adaptive filter based ona stable version of the Recursive Least Square (sRLS)algorithm was used in order to filter the ICs. sRLS wasadopted because it fmds the contamination coefficients thatrelate to recursively producing the least squares of the errorsignal. A full description of the sRLS algorithm is availablein [5].Table 1 summarizes the REG-ICA algorithm.

TABLE ISUMMARYOF REG-leAALGORITHM

D. Semi-SimulatedDataFifty four multichannel artifact - free EEG (pure EEG)

and EOG datasets were obtained from twenty seven healthysubjects. Each dataset lasts thirty seconds. The EEG signalswere recorded, while subjects had their eyes closed beforeand after an emotion evocative - stimuli experiment, fromnineteen electrodes, which were placed according to the 10-20 International System. More specifically electrodes wereplaced at Fpl, Fp2, F3, F4, F7, F8, Fz, C3, C4, Cz, T3, T4,T5, T6, P3, P4, Pz, 01, 02 . Electrodes with odd indiceswere referenced to the left mastoid and electrodes with evenindices were referenced to the right mastoid. Centralelectrodes (Fz,Cz, Pz) were referenced to the half of the sumof left and right mastoid. The EOG signals were recorded,while subjects had their eyes opened before and after theaforementioned experiment. Two EOG electrodes wereplaced above and below the left eye and another two on theouter canthi of each eye. From these electrodes two bipolarsignals were obtained, namely, vertical-EOG (VEOG),which is equal to upper minus lower electrode values andhorizontal-EOG, which is equal to left minus right EOGelectrode values. The sampling frequency is 200 Hz. Aband-pass filter in the frequency band between 0.5 and 40Hz was applied to EEG datasets, while the EOG signalswere filtered in the frequency band between 0.5 and 13 Hz

3. Reconstruct the signals by multiplying the ICs with theinverse of the mixing matrix

1. Apply ICA to decompose EEG signals to statisticallyindependent components. (Computation of the mixingmatrix)2. Filter the ICs with the sRLS adaptive filter. (Remove onlyocular artifacts)sRLS Parameters:

p =the order of adaptive filter (here p =3 )A= forgetting factor (here A= 0.9999 )a =value to initialize P(O) (here a =0.01 )

Input: Contaminated EEG and two (2) EOG channels as areference (VEOG, HEOG)Output: Clean EEG.

(4)im EEG. = EEG" 1 1a i ~ a ib i ~ b i

B. Regression AlgorithmEach regression - based AR technique, uses the followinglinear model (2) to calculate the relationship between the

observed EOG and EEG signals and tries to approximate the"real" EEG signals by subtracting the EOG signals from theobserved EEG (3).OBSj = EEGj+ajVEOG +bjHEOG (2)EEG. =OBS -a.VEOG-b.HEOG (3)1 1 1 1

where OBSjand EEGj are the observed and the real EEGsignals in the i th electrode. VEOG and HEOG are thevertical and horizontal EOG signals respectively, while thecontamination coefficients in the i th electrode are denotedwith aj and b., Finally the indicator /\ is used to clarify theapproximated variables.Adaptive filters are based on regression analysis. Theirgoal is to adjust the filter coefficients (a j , b ) and makethem approach the optimal filter coefficients (a j , b, ). Itstands that:


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Relative Error

Blink Segment

2900 2950 3000 3050Time (Sample Points)

. c - REG-ICA- LMS- Pure EEG

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Correlation Coefficient - Relative Error

TABLE IISUMMARY OFANOVA RESULTS

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Fig. 2 Blink Segment: Green and black lines depict contaminated EEG andpure EEG signals respectively, while blue and red lines illustrate thecleaned EEG with REG-ICA and LMS respectively. REG-ICA gives abetter approximation of pure EEG signals than LMS.

PerformanceIndices Statistics REG-leA LMS(mean:l:SD) (mean:l:SD)

Correlation F=53,799 -0.102:1:0.045 0.036:1:0.009Coefficient p=O.OOORoot Mean F=9,296 4.825:1:4.37 5.368:1:3 .66SQuareError p


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Root Mean Square Error in Time Domain philosophy because it uses EOG signals as the referencesignals and filters the ICs removing with that way onlyocular artifacts.A main drawback of the current study is the use of EOGsignals. All BSS-based AR techniques have the ability toreject ocular artifacts without the need of EOG signals, butthey introduce a substantial distortion of neural activity.According to our opinion, it is preferable to use morechannels for AR purposes, than distorting the brainresponses.Further research is necessary for the optimization of thecurrent AR approach . Different BSS algorithms anddifferent regression-based adaptive filters may be furtherinvestigated in order to conclude which is the optimalcombination of the regression and BSS methodology. Alsothe use of a fast BSS algorithm can promote the REG-ICA'sapplication to real life processes .

REFERENCES[I] DA Overton andC. Shagass. "Distribution of eye movement and eyeblink potentials over the scalp," Electroencephalography and ClinicalNeurophysiology, vol. 27, pp. 546, 1969.[2] F. Matsuo , 1.F. Peters and E .L. Reilly . "Elect rical phenomenaassociated with movements of the eyelid," Electroenceph. clin.Neurophysiol. , vol. 38, pp. 507-512,1975.[3] T.P Jung, C. Humphries, T.W. Lee, S. Makeig, et al. "Extended lCARemoves Artifacts from Electroencephalographic Recordings,"Advances in Neural Information Processing Systems 10, M. Jordan etal. Eds. , MIT Press,Cambridge USA, 1998.[4] A.J. Bell & TJ . Sejnowski. An information-maximization approach toblind separation and blind deconvolutiou, Neural Computation7:1129-1159,1995[5] P. He, G. Wilson and C. Russell , "Removal of ocular artifacts fromelectro-encephalogram by adaptive filtering" , Med. BioI. Eng.Comput., vol. 42, pp.407-412, 2004.[6] O.G. Lins, T.W. Picton, P. Berg and M. Scherg, "Ocular artefacts inrecording EEGs and event-related potentials: II. Source dipoles andsource components." Brain Topography, vol:6(l), pp. 65-78,1993.[7] MA Klados , C. L. Papadelis , C. Lithari and P.D. Bamidis , "TheRemoval of Ocular Artifacts From EEG signals: A Comparison of

Performances For Different Methods", 1. Vander Sioten, P. Verdonck,M. Nyssen, 1. Haueisen (Eds.): ECIFMBE 2008, IFMBE Proceedings22,pp.1259-1263,2008[8] N.P. Castellanos and VA Makarov, "Recovering EEG brain signals:Artifact suppression with wavelet enhanced independent componentanalysis," Journal of Neuroscience Methods, vol. 158, pp. 300-312,2006.[9] S.C. Ng and P. Raveendran, "Removal of EOG Artifacts Using ICARegression Method", NA Abu Osman, F. Ibrahim, WAB . WanAbas, H.S. Abd Rahman , H.N. Ting (Eds.) : Biomed 2008 ,Proceedings 21, pp. 226-229, 2008

V. CONCLUSIONSThe current study introduced a novel and automatic ARapproach. Results suggest that REG-ICA removes

successfully eye-blinking activity, while it distorts theunderlying cerebral activity less than other AR techniques.ACKNOWLEDGMENT

This work has been benefited by a grant from the GreekGENERAL SECRETARIAT FOR RESEARCH &TECHNOLOGY.

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A New Hybrid Method for EOG Artifact Rejection