Symmetrical EEG-FMRI Imaging by Sparse Regularization

Thomas Oberlin 1 Christian Barillot 2 Rémi Gribonval 3 Pierre Maurel 2
2 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
3 PANAMA - Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio
Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : This work considers the problem of brain imaging using simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). To this end, we introduce a linear coupling model that links the electrical EEG signal to the hemodynamic response from the blood-oxygen level dependent (BOLD) signal. Both modalities are then symmetrically integrated, to achieve a high resolution in time and space while allowing some robustness against potential decoupling of the BOLD effect. The novelty of the approach consists in expressing the joint imaging problem as a linear inverse problem, which is addressed using sparse regularization. We consider several sparsity-enforcing penalties, which naturally reflect the fact that only few areas of the brain are activated at a certain time, and allow for a fast optimization through proximal algorithms. The significance of the method and the effectiveness of the algorithms are demonstrated through numerical investigations on a spherical head model.
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Communication dans un congrès
EUSIPCO – 23rd European Signal Processing Conference, Aug 2015, Nice, France. pp. 1-5, 2015
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  • HAL Id : hal-01170889, version 1
  • OATAO : 14011

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Thomas Oberlin, Christian Barillot, Rémi Gribonval, Pierre Maurel. Symmetrical EEG-FMRI Imaging by Sparse Regularization. EUSIPCO – 23rd European Signal Processing Conference, Aug 2015, Nice, France. pp. 1-5, 2015. 〈hal-01170889〉

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