Skip to Main content Skip to Navigation
Journal articles

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.
Complete list of metadatas

Cited literature [9 references]  Display  Hide  Download

https://www.hal.inserm.fr/inserm-00540501
Contributor : Lotfi Senhadji <>
Submitted on : Friday, December 3, 2010 - 3:33:34 PM
Last modification on : Wednesday, May 16, 2018 - 11:23:08 AM
Long-term archiving on: : Friday, December 2, 2016 - 9:23:02 PM

Files

Detecting_causal_interdependen...
Files produced by the author(s)

Identifiers

Collections

Citation

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⟩

Share

Metrics

Record views

191

Files downloads

569