Independent Vector Analysis Applied to Remove Muscle Artifacts in EEG Data

Abstract

Electroencephalogram (EEG) data are often contaminated by various electrophysiological artifacts. Among all these artifacts, the muscle activity is particularly difficult to remove. In the literature, independent component analysis (ICA) and canonical correlation analysis (CCA), as blind source separation techniques, are the most popular methods. In this paper, we introduce a novel method for removing muscle artifacts in EEG data based on independent vector analysis. This method exploits both the second-order and higher order statistical information and thus takes advantage of both ICA and CCA. The proposed method is evaluated on realistic simulated data and is shown to significantly outperform ICA and CCA. In addition, the proposed method is applied on real ictal EEG data seriously contaminated with muscle artifacts. The proposed method is able to largely suppress muscle artifacts without altering the underlying EEG activity.