Partial mutual information for simple model order determination in multivariate EEG signals and its application to transfer entropy

The context of this work is the analysis of depth electroencephalographic signals recorded with depth electrodes during seizures in patients with drug-resistant epilepsy. Usually, different phases are observed during the seizure process and we aim to determine how cerebral structures get involved during these phases, in particular whether some structures can “drive” other ones. To this end, we consider a pair of signals and use transfer entropy which needs beforehand to choose efficiently the size of two conditioning vectors built on the past values of these signals. In this contribution, we extend a partial mutual information based technique, first developed for monochannel prediction models, to the case of two channels. Experimental results on signals generated either by a linear autoregressive model or by a physiology-based model of coupled neuronal populations support the relevance of the proposed approach.