Share Email Print
cover

Proceedings Paper

Blind separation of convolutive sEMG mixtures based on independent vector analysis
Author(s): Xiaomei Wang; Yina Guo; Wenyan Tian
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper 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.

Paper Details

Date Published: 8 December 2015
PDF: 6 pages
Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 98751S (8 December 2015); doi: 10.1117/12.2228722
Show Author Affiliations
Xiaomei Wang, Taiyuan Univ. of Science and Technology (China)
Yina Guo, Taiyuan Univ. of Science and Technology (China)
Wenyan Tian, Tiayuan Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 9875:
Eighth International Conference on Machine Vision (ICMV 2015)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev, Editor(s)

© SPIE. Terms of Use
Back to Top