Multi-modal EEG and fMRI Source Estimation Using Sparse Constraints

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

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

In this paper a multi-modal approach is presented and validated on real data to estimate the brain neuronal sources based on EEG and fMRI. Combining these two modalities can lead to source estimations with high spatio-temporal resolution. The joint method is based on the idea of linear model already presented in the literature where each of the data modalities are first modeled linearly based on the sources. Afterwards, they are integrated in a joint framework which also considers the sparsity of sources. The sources are then estimated with the proximal algorithm. The results are validated on real data and show the efficiency of the joint model compared to the uni-modal ones. We also provide a calibration solution for the system and demonstrate the effect of the parameter values for uni-and multi-modal estimations on 8 subjects.

Mots clés: eeg; fmri; sparsity

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

Saman Noorzadeh, Pierre Maurel, Thomas Oberlin, Rémi Gribonval, Christian Barillot. Multi-modal EEG and fMRI Source Estimation Using Sparse Constraints. MICCAI 2017 - 20th International Conference on Medical Image Computing and Computer Assisted Intervention, Sep 2017, Quebec, Canada. ⟨10.1007/978-3-319-66182-7_51⟩. ⟨hal-01586495⟩