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Title: Blind Source Separation for Surface Electromyograms Using a Bayesian Approach
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 SNR >= 14dB. 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.  more » « less
Award ID(s):
Author(s) / Creator(s):
Publisher / Repository:
Date Published:
Journal Name:
Proc. 30th European Signal Processing Conference
Page Range / eLocation ID:
1956 to 1960
Subject(s) / Keyword(s):
Blind source separation Bayesian classification Linear minimum mean-square error estimator Sparsity-aware processing
Medium: X
Belgrade, Serbia
Sponsoring Org:
National Science Foundation
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