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

Maxime Rio 1 Axel Hutt 1 Bernard Girau 1
1 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In the last decade, the analysis of the synchronization between different brain signals has attracted much attention. In this context, detection methods of amplitude synchrony computed on time-frequency maps consider the baseline activity before stimulus onset. The present work introduces a new method to detect subsets of synchronized channels that do not consider any baseline information. It 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.
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Submitted on : Monday, November 8, 2010 - 5:24:28 PM
Last modification on : Tuesday, June 9, 2020 - 11:10:03 AM
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Maxime Rio, Axel Hutt, Bernard Girau. Partial amplitude synchronization detection in brain signals using Bayesian Gaussian mixture models. Cinquième conférence plénière française de Neurosciences Computationnelles, "Neurocomp'10", Oct 2010, Lyon, France. pp.109 -- 113. ⟨hal-00534076⟩

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