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.
EEG signal transfer entropy physiology-based model causality
Chunfeng Yang Regine Le Bouquin Jeannes Jean-Jacques Bellanger Fabrice Wendling Huazhong Shu
U642, INSERM LTSI, Universite de Rennes 1 Rennes, France Centre de Recherche en Information Biomedic aboratory of Image Science and Technology, School of Computer Science and Engineering Southeast Univ
国际会议
上海
英文
700-704
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）