Abstract
The previous termdecomposition of surface electromyogramnext term data sets (s-EMG) is studied using blind source separation techniques based on sparseness; namely independent component previous termanalysis, sparsenext term nonnegative matrix factorization, and previous termsparsenext term component previous termanalysis.next term When applied to artificial previous termsignalsnext term we find ...
Abstract
The previous termdecomposition of surface electromyogramnext term data sets (s-EMG) is studied using blind source separation techniques based on sparseness; namely independent component previous termanalysis, sparsenext term nonnegative matrix factorization, and previous termsparsenext term component previous termanalysis.next term When applied to artificial previous termsignalsnext term we find noticeable differences of algorithm performance depending on the source assumptions. In particular, previous termsparsenext term nonnegative matrix factorization outperforms the other methods with regard to increasing additive noise. However, in the case of real s-EMG previous termsignalsnext term we show that despite the fundamental differences in the various models, the methods yield rather similar results and can successfully separate the source previous termsignal.next term 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.
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