Quantifying EEG synchrony using copulas

In this paper, we consider the problem of quantifying synchrony between multiple simultaneously recorded electroencephalographic signals. These signals exhibit nonlinear dependencies and non-Gaussian statistics. A copula based approach is presented to model the joint statistics. We then consider the application of copula derived synchrony measures for early diagnosis of Alzheimer's disease. Results on real data are presented.

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