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

Abstract : 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.
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-02472488
Contributor : Laurent Jonchère <>
Submitted on : Monday, February 10, 2020 - 11:43:09 AM
Last modification on : Tuesday, February 11, 2020 - 1:30:39 AM

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P.-A. Chantal, A. 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⟩

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