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On the use of sparse signal decomposition in the analysis of multi-channel surface electromyograms

Theis, Fabian J. ; García, G. A.



Abstract

The decomposition of surface electromyogram data sets (s-EMG) is studied using blind source separation techniques based on sparseness; namely independent component analysis, sparse nonnegative matrix factorization, and sparse component analysis. When applied to artificial signals we find noticeable differences of algorithm performance depending on the source assumptions. In particular, sparse ...

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