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

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84098

Hutt A, Munk,  M
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Rio, M., Hutt A, Munk, M., & Girau, B. (2011). Partial amplitude synchronization detection in brain signals using Bayesian Gaussian mixture models. Journal of Physiology, 105(1-3), 98-105. doi:10.1016/j.jphysparis.2011.07.018.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-BB64-C
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.