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Canonical Correlation Analysis Applied to Remove Muscle Artifacts From the Electroencephalogram

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5 Author(s)
Wim De Clercq ; Dept. of Electr. Eng., Katholieke Univ., Leuven ; Anneleen Vergult ; Bart Vanrumste ; Wim Van Paesschen
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The electroencephalogram (EEG) is often contaminated by muscle artifacts. In this paper, a new method for muscle artifact removal in EEG is presented, based on canonical correlation analysis (CCA) as a blind source separation (BSS) technique. This method is demonstrated on a synthetic data set. The method outperformed a low-pass filter with different cutoff frequencies and an independent component analysis (ICA)-based technique for muscle artifact removal. In addition, the method is applied on a real ictal EEG recording contaminated with muscle artifacts. The proposed method removed successfully the muscle artifact without altering the recorded underlying ictal activity

Published in:

IEEE Transactions on Biomedical Engineering  (Volume:53 ,  Issue: 12 )