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.
journal_name
J Comput Neuroscijournal_title
Journal of computational neuroscienceauthors
Daly I,Sweeney-Reed CM,Nasuto SJdoi
10.1007/s10827-012-0428-2subject
Has Abstractpub_date
2013-06-01 00:00:00pages
411-32issue
3eissn
0929-5313issn
1573-6873journal_volume
34pub_type
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