Contribution in analyzing directional propagation flow in EEG recordings investigating entropic methods and realistic physiological models

Our objective is to analyze EEG signals recorded with depth electrodes during seizures in patients with drug-resistant epilepsy. Usually, different phases are observed during the seizure process, including a fast onset activity (FOA). We aim to determine how cerebral structures get involved during this FOA, in particular whether some structures can “drive” some other structures. We compare a transfer entropy based measure with a measure related to linear Granger causality index to detect causal interdependences in multivariate signals generated either by a linear autoregressive model or by a physiology-based model of coupled neuronal populations. Experimental simulation results support the relevance of the new measure for characterizing the information flow for direct and indirect relations.