Removal of EMG and ECG artifacts from EEG based on wavelet transform and ICA

Weidong Zhou Weidong Zhou,J Gotman

doi:10.1109/iembs.2004.1403176

Removal of EMG and ECG artifacts from EEG based on wavelet transform and ICA

Weidong Zhou Weidong Zhou, J Gotman

https://doi.org/10.1109/iembs.2004.1403176

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Publication Date: Jan 1, 2004

Citations: 69

Affiliation: Shandong University, Montreal Neurological Institute and Hospital, McGill University

#Electrocardiograph Artifacts #Independent Component Analysis + Show 8 more

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Abstract

In this study, the methods of wavelet threshold de-noising and independent component analysis (ICA) are introduced. ICA is a novel signal processing technique based on high order statistics, and is used to separate independent components from measurements. The extended ICA algorithm does not need to calculate the higher order statistics, converges fast, and can be used to separate subGaussian and superGaussian sources. A pre-whitening procedure is performed to de-correlate the mixed signals before extracting sources. The experimental results indicate the electromyogram (EMG) and electrocardiograph (ECG) artifacts in electroencephalograph (EEG) can be removed by a combination of wavelet threshold de-noising and ICA.

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