Influence of signal preprocessing on ICA-Based EEG decomposition

Abstract

Independent Component Analysis (ICA) has been widely used for analysis of EEG data and separating brain and non-brain sources from the EEGmixture. In this study, we compared decomposition results of the most commonly applied ICA algorithms:AMICA, Extended-Infomax, Infomax and FastICA. We examined 12 conditions of EEG data pre-processing, and assessed the independence and physiological plausibility of the recovered components. The results demonstrate that, in general, there were no significant differences in the decomposition results, while data pre-processing choices had a much more pronounced effect. In conclusion the efficiency of the ICA decompositions is highly dependent on the pre-processing steps applied to the EEG data submitted to ICA, rather than type of ICA applied. © Springer International Publishing Switzerland 2014.