Multiresolution ICA for artifact identification from electroencephalographic recordings

Authors:
Nadia Mammone;Giuseppina Inuso;Fabio La Foresta;Francesco Carlo Morabito
Affiliations:
Neurolab, DIMET, University of Reggio Calabria, Reggio Calabria, Italy;Neurolab, DIMET, University of Reggio Calabria, Reggio Calabria, Italy;Neurolab, DIMET, University of Reggio Calabria, Reggio Calabria, Italy;Neurolab, DIMET, University of Reggio Calabria, Reggio Calabria, Italy
Venue:
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Year:
2007

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Abstract

This paper addresses the issue of artifact extraction from Electroencephalographic (EEG) signals and introduces a new technique for EEG artifact removal, based on the joint use of Wavelet transform and Independent Component Analysis (WICA). In fact, EEG recordings are often contaminated by the artifacts, signals that have non-cerebral origin and that might mimic cognitive or pathologic activity and therefore distort the analysis of EEG. The proposed technique extracts the artifacts taking into account the frequencies of the four major EEG rhythms. An artificial artifact-laden EEG dataset was created mixing a real EEG with a set of synthesized artifacts and the performance of WICA was measured. WICA had the best artifact separation performance for every kind of artifact with respect to other techniques and allowed for minimum information loss.