基于独立向量分析的脑电信号中肌电伪迹的去除方法

陈强; 陈勋; 余凤琼

doi:10.11999/JEIT160209

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

1.
(合肥工业大学生物医学工程系合肥 230009) ②(安徽医科大学医学心理学系合肥 230032)

基金项目:

国家自然科学基金(61501164, 81571760)

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- 被引次数: 0
出版历程
- 收稿日期: 2016-03-07
- 修回日期: 2016-07-18
- 刊出日期: 2016-11-19

Removal of Muscle Artifact from EEG Data Based on Independent Vector Analysis

1.
(Department of Biomedical Engineering, Hefei University of Technology, Hefei 230009, China)
2.
(Department of Medical Psychology, Anhui Medical University, Hefei 230032, China)

Funds:

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

摘要

摘要: 脑电数据经常被各种电生理信号伪迹所污染。在常见伪迹中，肌电伪迹特别难以去除。文献中最常用的方法包括诸如独立分量分析(Independent Component Analysis, ICA)和典型相关分析(Canonical Correlation Analysis, CCA)等盲源分离技术。该文首次提出一种基于独立向量分析(Independent Vector Analysis, IVA)的新方法，用以去除脑电中的肌电伪迹。IVA同时使用高阶统计量和二阶统计量，因此该方法能够充分利用肌电伪迹的非高斯性和弱相关性，兼具ICA方法和CCA方法的优势。实验表明，使用IVA方法可以在保留脑电成份的同时极大抑制肌电伪迹，效果显著优于ICA法和CCA法。
- 脑电 /
- 肌电伪迹 /
- 盲源分离 /
- 独立向量分析
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.
- ElectroEncephaloGram (EEG) /
- Muscle artifact /
- Blind Source Separation (BSS) /
- Independent Vector Analysis (IVA)

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参考文献(26)

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