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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
Contributor : Maxime Rio <>
Submitted on : Tuesday, October 18, 2011 - 3:58:57 PM
Last modification on : Friday, February 26, 2021 - 3:28:03 PM

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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. ⟨10.1016/j.jphysparis.2011.07.018⟩. ⟨inria-00633489⟩

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