Symmetrical EEG-FMRI Imaging by Sparse Regularization

Thomas Oberlin (1) , Christian Barillot (2) , Rémi Gribonval (3) , Pierre Maurel (2)

1 IRIT-SC - Signal et Communications
2 VisAGeS - Vision, Action et Gestion d'informations en Santé
3 PANAMA - Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio

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.

Mots clés: EEG-FMRI; Multimodal imaging; Structured sparsity; EEG inverse problem

Domaines: Traitement du signal et de l'image [eess.SP]

Thomas Oberlin, Christian Barillot, Rémi Gribonval, Pierre Maurel. Symmetrical EEG-FMRI Imaging by Sparse Regularization. 23rd European Signal Processing Conference (EUSIPCO 2015), Aug 2015, Nice, France. pp.1-5, ⟨10.1109/EUSIPCO.2015.7362708⟩. ⟨hal-01170889⟩