Number of sources uncertainty in blind source separation: application to EMG signal processing
| dc.contributor.author | Snoussi, Hichem | en |
| dc.contributor.author | Khanna, Saurabh | en |
| dc.contributor.author | Hewson, David | en |
| dc.contributor.author | Duchêne, Jacques | en |
| dc.date.accessioned | 2019-09-17T11:50:39Z | |
| dc.date.available | 2019-09-17T11:50:39Z | |
| dc.date.issued | 2007-10-22 | |
| dc.identifier.citation | Snoussi H, Khanna S, Hewson D, Duchene J (2007) 'Number of sources uncertainty in blind source separation: application to EMG signal processing', 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Lyon, IEEE. | en |
| dc.identifier.issn | 1094-687X | |
| dc.identifier.pmid | 18003518 | |
| dc.identifier.doi | 10.1109/IEMBS.2007.4353852 | |
| dc.identifier.uri | http://hdl.handle.net/10547/623471 | |
| dc.description.abstract | This contribution deals with the number of components uncertainty in blind source separation. The number of components is estimated by maximizing its marginal a posteriori probability which favors the simplest explanation of the observed data. Marginalizing (integrating over all the parameters) is implemented through the Laplace approximation based on an efficient wavelet spectral matching separating algorithm. The effectiveness of the proposed method is shown on EMG data processing. | |
| dc.language.iso | en | en |
| dc.publisher | IEEE | en |
| dc.relation.url | https://ieeexplore.ieee.org/document/4353852 | en |
| dc.subject | uncertainty | en |
| dc.subject | blind source separation | en |
| dc.subject | electromyography | en |
| dc.subject | medical signal processing | en |
| dc.title | Number of sources uncertainty in blind source separation: application to EMG signal processing | en |
| dc.type | Conference papers, meetings and proceedings | en |
| dc.identifier.journal | PROCEEDINGS OF THE 23RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-4 | en |
| dc.date.updated | 2019-09-17T10:46:53Z | |
| html.description.abstract | This contribution deals with the number of components uncertainty in blind source separation. The number of components is estimated by maximizing its marginal a posteriori probability which favors the simplest explanation of the observed data. Marginalizing (integrating over all the parameters) is implemented through the Laplace approximation based on an efficient wavelet spectral matching separating algorithm. The effectiveness of the proposed method is shown on EMG data processing. |