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hal-00534076, version 1

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

Maxime Rio () 1, Axel Hutt () 1, Bernard Girau () 1

Cinquième conférence plénière française de Neurosciences Computationnelles, "Neurocomp'10" (2010) 109 -- 113

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.

  • 1:  CORTEX (INRIA Lorraine - LORIA)
  • INRIA – CNRS : UMR7503 – Université Henri Poincaré - Nancy I – Université Nancy II – Institut National Polytechnique de Lorraine (INPL)
  • Domain : Cognitive science/Computer science
    Computer Science/Numerical Analysis
    Computer Science/Signal and Image Processing
    Computer Science/Learning
    Engineering Sciences/Signal and Image processing
  • Keywords : Amplitude synchronization – Partial synchronization – Morlet wavelet – Gaussian mixture model – Mean-field approximation
 
  • hal-00534076, version 1
  • http://hal.archives-ouvertes.fr/hal-00534076
  • oai:hal.archives-ouvertes.fr:hal-00534076
  • From: Maxime Rio <>
  • Submitted on: Monday, 8 November 2010 17:24:28
  • Updated on: Tuesday, 9 November 2010 10:39:12