Blind Separation of Surface Electromyographic Mixtures from Two Finger Extensor Muscles

Anton Dogadov 1 Christine Serviere 1 Franck Quaine 1
1 GIPSA-SAIGA - SAIGA
GIPSA-DA - Département Automatique, GIPSA-DIS - Département Images et Signal
Abstract : Blind source separation (BSS) was performed to reduce the crosstalk in the surface electromyografic signals (SEMG) for the muscle force estimation applications. A convolutive mixture model was employed to separate the SEMG signals from two finger extensor muscles using a frequency-domain approach. It was assumed that the tension of each muscle varies independently and the independence of the SEMG was replaced by minimization of the covar-iance of muscle forces represented by integrated SEMG. This covariance was also used to resolve the permutation ambiguity inherent to the frequency-domain BSS. The forces estimated by the reconstructed sources were compared with the measured forces to calculate the crosstalk reduction efficiency. The proposed algorithm was shown to be more effective in frequency domain than an ICA algorithm for extensor muscles crosstalk reduction.
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Emmanuel Vincent; Arie Yeredor; Zbyněk Koldovský; Petr Tichavský. 12th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2015), Aug 2015, Liberec, Czech Republic. Springer, 9237, pp.481-488, 2015, Lecture Notes in Computer Science. 〈10.1007/978-3-319-22482-4_56〉
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Anton Dogadov, Christine Serviere, Franck Quaine. Blind Separation of Surface Electromyographic Mixtures from Two Finger Extensor Muscles. Emmanuel Vincent; Arie Yeredor; Zbyněk Koldovský; Petr Tichavský. 12th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2015), Aug 2015, Liberec, Czech Republic. Springer, 9237, pp.481-488, 2015, Lecture Notes in Computer Science. 〈10.1007/978-3-319-22482-4_56〉. 〈hal-01191243〉

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