Blind separation of convolutive sEMG mixtures based on independent vector analysis
Xiaomei Wang, Yina Guo, Wenyan Tian · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2015
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.