title: Testing for significance of phase synchronisation dynamics in the EEG.
abstract: A number of tests exist to check for statistical significance of phase synchronisation within the Electroencephalogram (EEG); however, the majority suffer from a lack of generality and applicability. They may also fail to account for temporal dynamics in the phase synchronisation, regarding synchronisation as a constant state instead of a dynamical process. Therefore, a novel test is developed for identifying the statistical significance of phase synchronisation based upon a combination of work characterising temporal dynamics of multivariate time-series and Markov modelling. We show how this method is better able to assess the significance of phase synchronisation than a range of commonly used significance tests. We also show how the method may be applied to identify and classify significantly different phase synchronisation dynamics in both univariate and multivariate datasets.
authors.affiliation: Institute for Knowledge Discovery, Laboratory of Brain-Computer Interfaces, Graz University of Technology, Inffeldgasse 13/4, 8010, Graz, Austria. ian.daly@tugraz.at
authors.forename: Ian
authors.initials: I
authors.lastname: Daly
authors.forename: Catherine M
authors.initials: CM
authors.lastname: Sweeney-Reed
authors.forename: Slawomir J
authors.initials: SJ
authors.lastname: Nasuto
mesh_terms: D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D004569:Electroencephalography; D058407:Electroencephalography Phase Synchronization / Q000502:physiology*; D007839:Functional Laterality; D006801:Humans; D008390:Markov Chains; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D011597:Psychomotor Performance; D012372:ROC Curve; D013997:Time Factors