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inria-00633489, version 1

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

Maxime Rio () 1, Axel Hutt () 1, Matthias Munk 2, Bernard Girau () 1

Journal of Physiology - Paris 105, 1-3 (2011) 98-105

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.

  • 1:  CORTEX (INRIA Lorraine - LORIA)
  • INRIA – CNRS : UMR7503 – Université Henri Poincaré - Nancy I – Université Nancy II – Institut National Polytechnique de Lorraine (INPL)
  • 2:  Max Planck Institute for Brain Research
  • Max Planck Institute
  • Domain : Computer Science/Learning
    Computer Science/Signal and Image Processing
    Engineering Sciences/Signal and Image processing
    Cognitive science/Neuroscience
  • Keywords : Amplitude synchronization – Partial synchronization – Morlet wavelet – Gaussian mixture model – Mean-field approximation – Stability measure – Single-trial analysis
 
  • inria-00633489, version 1
  • http://hal.inria.fr/inria-00633489
  • oai:hal.inria.fr:inria-00633489
  • From: Maxime Rio <>
  • Submitted on: Tuesday, 18 October 2011 15:58:57
  • Updated on: Monday, 19 December 2011 15:28:36