title: On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP.
abstract: Standard artifact removal methods for electroencephalographic (EEG) signals are either based on Independent Component Analysis (ICA) or they regress out ocular activity measured at electrooculogram (EOG) channels. Successful ICA-based artifact reduction relies on suitable pre-processing. Here we systematically evaluate the effects of high-pass filtering at different frequencies. Offline analyses were based on event-related potential data from 21 participants performing a standard auditory oddball task and an automatic artifactual component classifier method (MARA). As a pre-processing step for ICA, high-pass filtering between 1-2 Hz consistently produced good results in terms of signal-to-noise ratio (SNR), single-trial classification accuracy and the percentage of `near-dipolar' ICA components. Relative to no artifact reduction, ICA-based artifact removal significantly improved SNR and classification accuracy. This was not the case for a regression-based approach to remove EOG artifacts.
authors.forename: Irene
authors.initials: I
authors.lastname: Winkler
authors.forename: Stefan
authors.initials: S
authors.lastname: Debener
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
authors.forename: Michael
authors.initials: M
authors.lastname: Tangermann
mesh_terms: D016477:Artifacts; D001921:Brain / Q000502:physiology; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D012815:Signal Processing, Computer-Assisted