Partial amplitude synchronization detection in brain signals using Bayesian Gaussian mixture models

Maxime Rio 1 Axel Hutt 1 Matthias Munk 2 Bernard Girau 1
1 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : The present work investigates instantaneous synchronization in multivariate signals. It introduces a new method to detect subsets of synchronized time series that do not consider any baseline information. The method is based on a Bayesian Gaussian mixture model applied at each location of a time-frequency map. The work assesses the relevance of detected subsets by a stability measure. The application to Local Field Potentials measured during a visuo-motor experiment in monkeys reveals a subset of synchronized time series measured in the visual cortex.
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https://hal.inria.fr/inria-00633489
Contributeur : Maxime Rio <>
Soumis le : mardi 18 octobre 2011 - 15:58:57
Dernière modification le : jeudi 11 janvier 2018 - 06:19:48

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Maxime Rio, Axel Hutt, Matthias Munk, Bernard Girau. Partial amplitude synchronization detection in brain signals using Bayesian Gaussian mixture models. Journal of Physiology - Paris, Elsevier, 2011, 105 (1-3), pp.98-105. 〈http://www.sciencedirect.com/science/article/pii/S0928425711000210〉. 〈10.1016/j.jphysparis.2011.07.018〉. 〈inria-00633489〉

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