Author:

Keywords:

Social Sciences, Science & Technology, Life Sciences & Biomedicine, Psychology, Biological, Neurosciences, Physiology, Psychology, Psychology, Experimental, Neurosciences & Neurology, EEG, ERP, Artifact removal, Validation, Blink artifact, Independent component analysis, INDEPENDENT COMPONENT ANALYSIS, EVENT-RELATED POTENTIALS, OCULAR ARTIFACTS, DYNAMICS, EXTRACTION, SEPARATION, FMRI, Adult, Artifacts, Cues, Data Interpretation, Statistical, Electroencephalography, Evoked Potentials, Eye Movements, Female, Humans, Male, Photic Stimulation, Principal Component Analysis, Reproducibility of Results, Signal Processing, Computer-Assisted, Young Adult, STADIUS-10-293, 06 Biological Sciences, 11 Medical and Health Sciences, 17 Psychology and Cognitive Sciences, Experimental Psychology, 31 Biological sciences, 32 Biomedical and clinical sciences, 52 Psychology

Abstract:

Abstract Eye movement artifacts in electroencephalogram (EEG) recordings can greatly distort grand mean event-related potential (ERP) waveforms. Different techniques have been suggested to remove these artifacts prior to ERP analysis. Independent component analysis (ICA) is suggested as an alternative method to "filter" eye movement artifacts out of the EEG, preserving the brain activity of interest and preserving all trials. However, the identification of artifact components is not always straightforward. Here, we compared eye movement artifact removal by ICA compiled on 10 s of EEG, on eye movement epochs, or on the complete EEG recording to the removal of eye movement artifacts by rejecting trials or by the Gratton and Coles method. ICA performed as well as the Gratton and Coles method. By selecting only eye movement epochs for ICA compilation, we were able to facilitate the identification of components representing eye movement artifacts.