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

Aurelien Hazart, Olivier Féron, Andrzej Cichocki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationMachine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009
DOIs
Publication statusPublished - 2009
Externally publishedYes
EventMachine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009 - Grenoble, France
Duration: 2 Sep 20094 Sep 2009

Publication series

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

Conference

ConferenceMachine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009
Country/TerritoryFrance
CityGrenoble
Period2/09/094/09/09

Keywords

  • Eegimeg inverse problem
  • Markov random field
  • Piecewise latent process

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