title: Partial amplitude synchronization detection in brain signals using Bayesian Gaussian mixture models.
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.
authors.affiliation: INRIA-Nancy Grand Est Research Center, Cortex Group, France. maxime.rio@loria.fr
authors.forename: Maxime
authors.initials: M
authors.lastname: Rio
authors.forename: Axel
authors.initials: A
authors.lastname: Hutt
authors.forename: Matthias
authors.initials: M
authors.lastname: Munk
authors.forename: Bernard
authors.initials: B
authors.lastname: Girau
mesh_terms: D000818:Animals; D001921:Brain / Q000502:physiology*; D000882:Haplorhini; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*