Blind source separation of underdetermined mixtures of event-related sources

Mohammad Niknazar 1 Hanna Becker 2, 3 Bertrand Rivet 1 Christian Jutten 1 Pierre Comon 3
1 GIPSA-VIBS - VIBS
GIPSA-DIS - Département Images et Signal
3 GIPSA-CICS - CICS
GIPSA-DIS - Département Images et Signal
Abstract : This paper addresses the problem of blind source separation for underdetermined mixtures (i.e., more sources than sensors) of event-related sources that include quasi-periodic sources (e.g., electrocardiogram (ECG)), sources with synchronized trials (e.g., event-related potentials (ERP)), and amplitude-variant sources. The proposed method is based on two steps: (i) tensor decomposition for underdetermined source separation and (ii) signal extraction by Kalman filtering to recover the source dynamics. A tensor is constructed for each source by synchronizing on the ''event'' period of the corresponding signal and stacking different periods along the second dimension of the tensor. To cope with the interference from other sources that impede on the extraction of weak signals, two robust tensor decomposition methods are proposed and compared. Then, the state parameters used within a nonlinear dynamic model for the extraction of event-related sources from noisy mixtures are estimated from the loading matrices provided by the first step. The influence of different parameters on the robustness to outliers of the proposed method is examined by numerical simulations. Applied to clinical electroencephalogram (EEG), ECG and magnetocardiogram (MCG), the proposed method exhibits a significantly higher performance in terms of expected signal shape than classical source separation methods such as piCA and FastICA.
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Article dans une revue
Signal Processing, Elsevier, 2014, 101, pp.52-64. <10.1016/j.sigpro.2014.01.031>


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Soumis le : mercredi 26 février 2014 - 10:22:04
Dernière modification le : lundi 14 septembre 2015 - 09:25:08
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Mohammad Niknazar, Hanna Becker, Bertrand Rivet, Christian Jutten, Pierre Comon. Blind source separation of underdetermined mixtures of event-related sources. Signal Processing, Elsevier, 2014, 101, pp.52-64. <10.1016/j.sigpro.2014.01.031>. <hal-00952039>

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