Removal of Muscle Artifact from EEG Data Based on Independent Vector Analysis
CHEN Qiang① CHEN Xun① YU Fengqiong②
①(Department of Biomedical Engineering, Hefei University of Technology, Hefei 230009, China) ②(Department of Medical Psychology, Anhui Medical University, Hefei 230032, China)
ElectroEncephaloGram (EEG) data are often contaminated by various electrophysiological artifacts. Among all these artifacts, removing the ones related to muscle activity is particularly challenging. In past studies, Independent Component Analysis (ICA) and Canonical Correlation Analysis (CCA), as Blind Source Separation (BSS) methods, are widely used. In this work, a new method for muscle artifact removal in EEG data using Independent Vector Analysis (IVA) is proposed. IVA utilizes both the higher-order and second-order statistics, so that it makes full use of non-Gaussianity and weak autocorrelation of the muscle artifact and has the advantages of both ICA and CCA. The proposed method is examined on a number of simulated data sets and is shown to have better performance than ICA and CCA. The proposed IVA method is able to largely suppress muscle activity and meanwhile well preserve the underlying EEG activity.
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