Bibtex de la publication

@InProceedings{ Ob2015.2,
author = {Oberlin, Thomas and Barillot, Christian and Gribonval, RĂ©mi and Maurel, Pierre},
title = "{Symmetrical EEG--fMRI Imaging by Sparse Regularization (regular paper)}",
booktitle = "{European Signal and Image Processing Conference (EUSIPCO), Nice, 31/08/2015-04/09/2015}",
year = {2015},
month = {septembre},
publisher = {IEEE},
address = {http://www.ieee.org/},
pages = {1870--1874},
language = {anglais},
URL = {http://www.irit.fr/publis/SC/Oberlin_Eusipco15.pdf},
keywords = {EEG-fMRI; multimodal imaging; structured sparsity; EEG inverse problem},
abstract = {his 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.}
}