Detection of Phase Synchronization in Multivariate Single Brain Signals by a Clustering Approach

  • Axel Hutt
  • Matthias H.J. Munk
Part of the Springer Series in Computational Neuroscience book series (NEUROSCI, volume 2)


Analog signals of the cerebral cortex in behaving subjects frequently express strong oscillatory components. To investigate functional interactions among different areas of the cortex, it is biologically plausible to determine dependencies of oscillatory signals such as their phase relation both within and across areas. The chapter introduces a cluster approach algorithm to detect phase synchronization in single brain signals. The introduced synchronization index allows for the extraction of time windows, which exhibit strong phase synchronization in all examined time series. This kind of phase synchronization is highly non-stationary and is called mutual phase synchronization. Further the assessment of single trials with respect to the trial average revealed that a number of features in time–frequency space are common to different trials.


Phase Difference Cluster Centre Cluster Result Single Trial Phase Synchronization 
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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.INRIA CR Nancy – Grand Est, CS20101Villers-ls-Nancy CedexFrance
  2. 2.Max-Planck-Institute for Biological CyberneticsTuebingenGermany

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