Blind Source Separation for Surface Electromyograms Using a Bayesian Approach
Blind Source Separation for Surface Electromyograms Using a Bayesian Approach
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DOI：
10.23919/eusipco55093.2022.9909936
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
M. Aboufazeli;John Mathews
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文献类型：
--
作者：
M. Aboufazeli;John Mathews
This paper presents a blind source separation algorithm to identify binary and sparse sources from convolutive mixtures with linear and time-invariant finite impulse responses. Our approach combines Bayesian algorithms for detecting source activity with a linear minimum mean-square error estimator to identify all the time samples when each source is active. The algorithm was implemented on simulated electromyo-grams to identify neural commands. Our algorithm identified more than 96% of the sources on average with 16 or more measurement channels and $\text{SNR}\geq 14\text{dB}$. For the detected sources, this algorithm correctly identified more than 94% of the samples on average. This performance was significantly better than that of a competing algorithm available in the literature.
