Detecting causal interdependence in simulated neural signals based on pairwise and multivariate analysis.

Abstract : 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 structure can "drive" some other structures. This paper focuses on a linear Granger causality based measure to detect causal relation of interdependence in multivariate signals generated by a physiology-based model of coupled neuronal populations. When coupling between signals exists, statistical analysis supports the relevance of this index for characterizing the information flow and its direction among neuronal populations.
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Conference proceedings : .. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, Institute of Electrical and Electronics Engineers (IEEE), 2010, 1, pp.162-5. 〈10.1109/IEMBS.2010.5627241〉
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Chufeng Yang, Régine Le Bouquin Jeannes, Gérard Faucon, Fabrice Wendling. Detecting causal interdependence in simulated neural signals based on pairwise and multivariate analysis.. Conference proceedings : .. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, Institute of Electrical and Electronics Engineers (IEEE), 2010, 1, pp.162-5. 〈10.1109/IEMBS.2010.5627241〉. 〈inserm-00540501〉

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