Inferring effective connectivity using robust low-rank canonical polyadic decomposition Application to epileptic intracerebral EEG signals

P.-A. Chantal (1) , Ahmad Karfoul (1) , A. Pasnicu (2) , R. Le Bouquin Jeannès (1)

1 LTSI - Laboratoire Traitement du Signal et de l'Image
2 Centre Hospitalier Universitaire de Rennes [CHU Rennes] = Rennes University Hospital [Ponchaillou]

This paper addresses the estimation of effective connectivity in epileptic brain networks. To this end, a constrained Canonical Polyadic Decomposition (CPD) of a Partial Directed Coherence tensor issued from a modeling of the observed data is investigated. The efficiency of the proposed approach is confirmed in the context of epileptic intracerebral electroencephalographic (iEEG) signals. © 2019 IEEE.

Mots clés: Biomedical engineering; Brain networks; Canonical polyadic decompositions; Effective connectivities; Intra-cerebral EEG; Observed data; Partial directed coherence; Electroencephalography

Domaines: Ingénierie biomédicale

P.-A. Chantal, Ahmad Karfoul, A. Pasnicu, R. Le Bouquin Jeannès. Inferring effective connectivity using robust low-rank canonical polyadic decomposition Application to epileptic intracerebral EEG signals. 5th International Conference on Advances in Biomedical Engineering, ICABME 2019, Oct 2019, Tripoli, Lebanon. pp.8940164, ⟨10.1109/ICABME47164.2019.8940164⟩. ⟨hal-02472488⟩