基于独立向量分析的脑电信号中肌电伪迹的去除方法

doi:10.11999/JEIT160209

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Abstract

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.

Key words： ElectroEncephaloGram (EEG) Muscle artifact Blind Source Separation (BSS) Independent Vector Analysis (IVA)

Received: 07 March 2016 Published: 30 September 2016

PACS:	TP391
	R741.044

Fund:

The National Natural Science Foundation of China (61501164, 81571760)

Corresponding Authors: CHEN Xun E-mail: xunchen@ece.ubc.ca

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	CHEN Qiang
	CHEN Xun
	YU Fengqiong

Cite this article:

CHEN Qiang,CHEN Xun,YU Fengqiong. Removal of Muscle Artifact from EEG Data Based on Independent Vector Analysis[J]. JEIT, 2016, 38(11): 2840-2847.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT160209 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I11/2840

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