Blind Separation of Convolutive sEMG Mixtures based on Independent Vector Analysis

被引：0

作者：

Wang, Xiaomei ^{[1
]}

Guo, Yina ^{[1
]}

Tian, Wenyan ^{[1
]}

机构：

[1] Taiyuan Univ Sci & Technol, Taiyuan 030024, Shanxi, Peoples R China

来源：

EIGHTH INTERNATIONAL CONFERENCE ON MACHINE VISION (ICMV 2015) | 2015年 / 9875卷

关键词：

Convolutive mixtures; blind source separation (BSS); surface electromyography (sEMG); independent vector analysis (IVA);

D O I：

10.1117/12.2228722

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An independent vector analysis (IVA) method base on variable-step gradient algorithm is proposed in this paper. According to the sEMG physiological properties, the IVA model is applied to the frequency-domain separation of convolutive sEMG mixtures to extract motor unit action potentials information of sEMG signals. The decomposition capability of proposed mehod is compared to the one of independent component analysis (ICA), and experimental results show the variable-step gradient IVA method outperforms ICA in blind separation of convolutive sEMG mixtures.

引用

页数：6

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