Theis, Fabian J. and Garcia, Gonzalo A. (2006) On the use of sparse signal decomposition in the analysis of multi-channel surface electromyograms. SIGNAL PROCESSING, 86 (3). pp. 603-623. ISSN 0165-1684, 1872-7557
Full text not available from this repository. (Request a copy)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 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 l-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. (C) 2005 Elsevier B.V. All rights reserved.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | INDEPENDENT COMPONENT ANALYSIS; BLIND SOURCE SEPARATION; SKELETAL-MUSCLE; ALGORITHM; DIAGONALIZATION; EMG; surface EMG; blind source separation; sparse component analysis; independent component analysis; sparse nonnegative matrix factorization |
| Subjects: | 500 Science > 570 Life sciences |
| Divisions: | Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang > Arbeitsgruppe Dr. Fabian Theis |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 22 Feb 2021 09:24 |
| Last Modified: | 22 Feb 2021 09:24 |
| URI: | https://pred.uni-regensburg.de/id/eprint/34922 |
Actions (login required)
 |
View Item |
