Automatic Artifact Rejection From Multichannel Scalp EEG by Wavelet ICA

Abstract— Electroencephalographic (EEG) recordings are often contaminated by artifacts, i.e., signals with noncerebral origin that might mimic some cognitive or pathologic activity, this way affecting the clinical interpretation of traces. Artifact rejection is, thus, a key analysis for both visual inspection and digital processing of EEG. Automatic artifact rejection is needed for effective real time inspection because manual rejection is time consuming. In this paper, a novel technique (Automatic Wavelet Independent Component Analysis, AWICA) for automatic EEG artifact removal is presented. Through AWICA we claim to improve the performance and fully automate the process of artifact removal fromscalp EEG. AWICA is based on the joint use of the Wavelet Transform and of ICA: it consists of a two-step procedure relying on the concepts of kurtosis and Renyi’s entropy. Both synthesized and real EEG data are processed by AWICA and the results achieved were compared to the ones obtained by applying to the same data the “wavelet enhanced” ICA method recently proposed by other authors. < final year projects >