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

Abstract

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.