A Performance Study of various Brain Source Imaging Approaches

Hanna Becker 1, 2 Laurent Albera 3, 4, * Pierre Comon 2, * Rémi Gribonval 4 Fabrice Wendling 3 Isabelle Merlet 3
* Auteur correspondant
2 GIPSA-CICS - CICS
GIPSA-DIS - Département Images et Signal
4 PANAMA - Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio
IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE, Inria Rennes – Bretagne Atlantique
Abstract : The objective of brain source imaging consists in reconstructing the cerebral activity everywhere within the brain based on EEG or MEG measurements recorded on the scalp. This requires solving an ill-posed linear inverse problem. In order to restore identifiability, additional hypotheses need to be imposed on the source distribution, giving rise to an impressive number of brain source imaging algorithms. However, a thorough comparison of different methodologies is still missing in the literature. In this paper, we provide an overview of priors that have been used for brain source imaging and conduct a comparative simulation study with seven representative algorithms corresponding to the classes of minimum norm, sparse, tensor-based, subspace-based, and Bayesian approaches. This permits us to identify new benchmark algorithms and promising directions for future research.
Type de document :
Communication dans un congrès
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2014), May 2014, Florence, Italy. IEEE, pp.5910-5914, 2014, <10.1109/ICASSP.2014.6854729>
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Dernière modification le : mercredi 2 août 2017 - 10:10:05
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Hanna Becker, Laurent Albera, Pierre Comon, Rémi Gribonval, Fabrice Wendling, et al.. A Performance Study of various Brain Source Imaging Approaches. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2014), May 2014, Florence, Italy. IEEE, pp.5910-5914, 2014, <10.1109/ICASSP.2014.6854729>. <hal-00990273v2>

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