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

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.

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