基于獨(dú)立向量分析的腦電信號(hào)中肌電偽跡的去除方法

陳強(qiáng); 陳勛; 余鳳瓊

doi:10.11999/JEIT160209

基于獨(dú)立向量分析的腦電信號(hào)中肌電偽跡的去除方法

doi: 10.11999/JEIT160209

1.
(合肥工業(yè)大學(xué)生物醫(yī)學(xué)工程系合肥 230009) ②(安徽醫(yī)科大學(xué)醫(yī)學(xué)心理學(xué)系合肥 230032)

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61501164, 81571760)

計(jì)量
- 文章訪問(wèn)數(shù): 1902
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- PDF下載量: 855
- 被引次數(shù): 0
出版歷程
- 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: 腦電數(shù)據(jù)經(jīng)常被各種電生理信號(hào)偽跡所污染。在常見(jiàn)偽跡中，肌電偽跡特別難以去除。文獻(xiàn)中最常用的方法包括諸如獨(dú)立分量分析(Independent Component Analysis, ICA)和典型相關(guān)分析(Canonical Correlation Analysis, CCA)等盲源分離技術(shù)。該文首次提出一種基于獨(dú)立向量分析(Independent Vector Analysis, IVA)的新方法，用以去除腦電中的肌電偽跡。IVA同時(shí)使用高階統(tǒng)計(jì)量和二階統(tǒng)計(jì)量，因此該方法能夠充分利用肌電偽跡的非高斯性和弱相關(guān)性，兼具ICA方法和CCA方法的優(yōu)勢(shì)。實(shí)驗(yàn)表明，使用IVA方法可以在保留腦電成份的同時(shí)極大抑制肌電偽跡，效果顯著優(yōu)于ICA法和CCA法。
- 腦電 /
- 肌電偽跡 /
- 盲源分離 /
- 獨(dú)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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參考文獻(xiàn)(26)

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