TY - GEN

T1 - Hierarchical model with piecewise latent process for globally sparse / locally smooth brain generators imaging

AU - Hazart, Aurelien

AU - Féron, Olivier

AU - Cichocki, Andrzej

PY - 2009

Y1 - 2009

N2 - Noninvasive measurement techniques like EEG (electroencephalography) or MEG (magnetoencephalography) provide a good time resolution but suffer of a lack of spatial resolution. Source reconstruction is a solution for increasing the spatial resolution. It requires to solve an ill-posed inverse problem where the challenge is to restrict the source space, making a compromise between smooth and sparse constraints. We propose a model that introduces a piecewise latent process to ensure local homogeneity and global sparsity of the source. The method is developed in a Bayesian framework and the source reconstruction is expressed as the minimum mean square error, computed with a Markov Chain Monte Carlo algorithm. In addition to the source reconstruction, the method also provides a segmented solution that can be relevant for classification issues. The main contribution is the novel application of such a probabilistic model and its comparison with existing approaches. We apply the method on simulated EEG recordings and show the positive influence of the latent process.

AB - Noninvasive measurement techniques like EEG (electroencephalography) or MEG (magnetoencephalography) provide a good time resolution but suffer of a lack of spatial resolution. Source reconstruction is a solution for increasing the spatial resolution. It requires to solve an ill-posed inverse problem where the challenge is to restrict the source space, making a compromise between smooth and sparse constraints. We propose a model that introduces a piecewise latent process to ensure local homogeneity and global sparsity of the source. The method is developed in a Bayesian framework and the source reconstruction is expressed as the minimum mean square error, computed with a Markov Chain Monte Carlo algorithm. In addition to the source reconstruction, the method also provides a segmented solution that can be relevant for classification issues. The main contribution is the novel application of such a probabilistic model and its comparison with existing approaches. We apply the method on simulated EEG recordings and show the positive influence of the latent process.

KW - Eegimeg inverse problem

KW - Markov random field

KW - Piecewise latent process

UR - http://www.scopus.com/inward/record.url?scp=77950923336&partnerID=8YFLogxK

U2 - 10.1109/MLSP.2009.5306218

DO - 10.1109/MLSP.2009.5306218

M3 - Conference contribution

AN - SCOPUS:77950923336

SN - 9781424449484

T3 - Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009

BT - Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009

T2 - Machine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009

Y2 - 2 September 2009 through 4 September 2009

ER -