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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Influence of Signal Preprocessing on ICA-Based EEG Decomposition
verfasst von : Z. Zakeri, S. Assecondi, A. P. Bagshaw, T. N. Arvanitis
Erschienen in: XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013
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Independent Component Analysis (ICA) has been widely used for analysis of EEG data and separating brain and non-brain sources from the EEG mixture. 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.