Validation of ICA as a tool to remove eye movement artifacts from EEG/ERP

AbstractEye movement artifacts in electroencephalogram (EEG) recordings can greatly distort grand mean event-relatedpotential(ERP)waveforms.DifferenttechniqueshavebeensuggestedtoremovetheseartifactspriortoERPanalysis.Independentcomponentanalysis(ICA)issuggestedasanalternativemethodto‘‘filter’’eyemovementartifactsoutofthe EEG, preserving the brain activity of interest and preserving all trials. However, the identification of artifactcomponents is not always straightforward. Here, we compared eye movement artifact removal by ICA compiled on10sofEEG,oneyemovementepochs,oronthecompleteEEGrecordingtotheremovalofeyemovementartifactsbyrejecting trials or by the Gratton and Coles method. ICA performed as well as the Gratton and Coles method. Byselecting only eye movement epochs for ICA compilation, we were able to facilitate the identification of componentsrepresenting eye movement artifacts.Descriptors: EEG/ERP, Artifact removal, Validation, Blink artifact, Independent component analysisThe increasing popularity of the electroencephalogram (EEG)and event-related potentials (ERP) for research and clinical pur-poses has resulted in better acquisition devices minimizing theamount of noise picked up during an EEG recording. However,an important source of noise that cannot be avoided by betteramplifiers or electromagnetically shielded rooms is the electricalactivity that is associated with eye movements. Among eyemovements, blinks cause the largest distortions, mainly becauseof the movement of the eyelids across the surface of the eyes,but also saccades can cause large distortions in EEG signals andERPwaveforms,particularlyatfrontalelectrodes(Iwasakietal.,2005).Becausetheseartifactscanhampercorrectinterpretation,itisbest to remove them from the data. In most ERP studies this isdone by rejecting trialscontaining these artifacts. Such trialscanbedetectedbysettinganabsoluteamplitudethreshold.However,the amount of data lost by rejecting trials containing eye move-ments can be unacceptably high, especially when one is workingwith clinical populations or children, for whom it is difficult torefrain from blinking. A method that allows the cleaning up ofthe contaminated trials by filtering or removing the eye move-ment artifacts from the data would, therefore, be highly bene-ficialinterms ofresearcheffortefficiency. Consequently, severalmethods that try to remove as much of the eye movementartifacts without reducing data quality have been introduced(Berg & Scherg, 1994; Croft & Barry, 2000; Gratton, Coles, DFord,Sands,LFrankFJunget al., 2000a, 2000b; Vigario, 1997). ICA is a data-driven blindsourceseparationmethodthatisappliedonbiomedicaldatasuchas EEG, ERP, magnetoencephalography, and functional mag-netic resonance imaging (fMRI; Bell & Sejnowski, 1995; Stone,2002;Vigario,Sarela,Jousmaki,Hamalainen,O Iidaka, Matsumoto, Nogawa, Yamamoto, & Sadato,2006; Kansaku et al., 2005; Makeig et al., 2004; Zeki, Perry, &Bartels, 2003).It appears however, that only few research groups effectivelyapply ICA or other blind source separation algorithms (e.g.,SOBI,JADE)toremoveeyemovementartifactsfromtheirEEG