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