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

Fabian J. Theis, Gonzalo A. García

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


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 nonnegative matrix factorization outperforms the other methods with regard to increasing additive noise. However, in the case of real s-EMG signals we show that despite the fundamental differences in the various models, the methods yield rather similar results and can successfully separate the source signal. This can be explained by the fact that the different sparseness assumptions (super-Gaussianity, positivity together with minimal 1-norm and fixed number of zeros, respectively) are all only approximately fulfilled thus apparently forcing the algorithms to reach similar results, but from different initial conditions.

Original languageEnglish
Pages (from-to)603-623
Number of pages21
JournalSignal Processing
Issue number3
StatePublished - Mar 2006
Externally publishedYes


  • Blind source separation
  • Independent component analysis
  • Sparse component analysis
  • Sparse nonnegative matrix factorization
  • Surface EMG


Dive into the research topics of 'On the use of sparse signal decomposition in the analysis of multi-channel surface electromyograms'. Together they form a unique fingerprint.

Cite this