Localization of spatially distributed brain sources after a tensor-based preprocessing of interictal epileptic EEG data

Laurent Albera (1, 2) , H Becker (2, 3) , Ahmad Karfoul (2) , Rémi Gribonval (1) , A Kachenoura (2) , S Bensaid (2) , Lotfi Senhadji (2) , A Hernandez (2) , I Merlet (2)

1 PANAMA - Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio
2 LTSI - Laboratoire Traitement du Signal et de l'Image
3 Technicolor R & I [Cesson Sévigné]

This paper addresses the localization of spatially distributed sources from interictal epileptic electroencephalo-graphic data after a tensor-based preprocessing. Justifying the Canonical Polyadic (CP) model of the space-time-frequency and space-time-wave-vector tensors is not an easy task when two or more extended sources have to be localized. On the other hand, the occurrence of several amplitude modulated spikes originating from the same epileptic region can be used to build a space-time-spike tensor from the EEG data. While the CP model of this tensor appears more justified, the exact computation of its loading matrices can be limited by the presence of highly correlated sources or/and a strong background noise. An efficient extended source localization scheme after the tensor-based preprocessing has then to be set up. Different strategies are thus investigated and compared on realistic simulated data: the "disk algorithm" using a precomputed dictionary of circular patches, a standardized Tikhonov regularization and a fused LASSO scheme.

Domaines: Ingénierie biomédicale

Laurent Albera, H Becker, Ahmad Karfoul, Rémi Gribonval, A Kachenoura, et al.. Localization of spatially distributed brain sources after a tensor-based preprocessing of interictal epileptic EEG data. 2015 37th annual international conference of the IEEE Engineering in Medicine and Biology Society, Aug 2015, Milan, Italy. pp.6995 - 6998. ⟨hal-01245239⟩