Number of sources uncertainty in blind source separation: application to EMG signal processing

David Hewson; Hichem Snoussi; Saurabh Khanna; Jacques Duchêne

doi:10.1109/IEMBS.2007.4353852

Number of sources uncertainty in blind source separation: application to EMG signal processing

David Hewson
, Hichem Snoussi
, Saurabh Khanna
, Jacques Duchêne

Institute of Health Research (IHR)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	nan
Publisher	Institute of Electrical and Electronics Engineers Inc.
DOIs	https://doi.org/10.1109/IEMBS.2007.4353852
Publication status	Published - 22 Oct 2007
Event	29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Lyon Duration: 22 Aug 2007 → 26 Aug 2007

Conference

Conference	29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
City	Lyon
Period	22/08/07 → 26/08/07
Other	29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (22/08/2007-26/08/2007, Lyon)

Keywords

Electromyography
Uncertainty
blind source separation
medical signal processing

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https://ieeexplore.ieee.org/document/4353852

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title = "Number of sources uncertainty in blind source separation: application to EMG signal processing",

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.",

keywords = "Electromyography, Uncertainty, blind source separation, medical signal processing",

author = "David Hewson and Hichem Snoussi and Saurabh Khanna and Jacques Duch{\^e}ne",

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note = "29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society ; Conference date: 22-08-2007 Through 26-08-2007",

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T1 - Number of sources uncertainty in blind source separation: application to EMG signal processing

AU - Hewson, David

AU - Snoussi, Hichem

AU - Khanna, Saurabh

AU - Duchêne, Jacques

PY - 2007/10/22

Y1 - 2007/10/22

N2 - 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.

AB - 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.

KW - Electromyography

KW - Uncertainty

KW - blind source separation

KW - medical signal processing

U2 - 10.1109/IEMBS.2007.4353852

DO - 10.1109/IEMBS.2007.4353852

M3 - Conference contribution

BT - nan

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Y2 - 22 August 2007 through 26 August 2007

ER -

Number of sources uncertainty in blind source separation: application to EMG signal processing

Abstract

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Keywords

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