Blind separation of convolutive sEMG mixtures based on independent vector analysis

Xiaomei Wang; Yina Guo; Wenyan Tian

doi:10.1117/12.2228722

8 December 2015 Blind separation of convolutive sEMG mixtures based on independent vector analysis

Xiaomei Wang, Yina Guo, Wenyan Tian

Proceedings Volume 9875, Eighth International Conference on Machine Vision (ICMV 2015); 98751S (2015) https://doi.org/10.1117/12.2228722
Event: Eighth International Conference on Machine Vision, 2015, Barcelona, Spain

Abstract

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 method 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.

Citation Download Citation

Xiaomei Wang, Yina Guo, and Wenyan Tian "Blind separation of convolutive sEMG mixtures based on independent vector analysis", Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 98751S (8 December 2015); https://doi.org/10.1117/12.2228722

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