@article{Daly2012phase,

title = {Testing for significance of phase synchronisation dynamics in the EEG},

author = {Ian Daly and Catherine Sweeney-Reed and Slawomir J. Nasuto},

url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/Testing-for-significance-of-phase-synchronisation-dynamics-in.pdf},

doi = {10.1007/s10827-012-0428-2},

year  = {2013},

date = {2013-06-01},

journal = {Journal of Computational Neuroscience},

volume = {34},

number = {3},

pages = {411-432},

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.},

keywords = {EEG, Functional connectivity, Markov models, Semi-Markov models, Significance testing},

pubstate = {published},

tppubtype = {article}

}