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Testing for time-localized coherence in bivariate data

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Article number046205
<mark>Journal publication date</mark>9/04/2012
<mark>Journal</mark>Physical Review E
Issue number4
Number of pages16
Pages (from-to)-
Publication StatusPublished
<mark>Original language</mark>English


We present a method for the testing of significance when evaluating the coherence of two oscillatory time series that may have variable amplitude and frequency. It is based on evaluating the self-correlations of the time series. We demonstrate our approach by the application of wavelet-based coherence measures to artificial and physiological examples. Because coherence measures of this kind are strongly biased by the spectral characteristics of the time series, we evaluate significance by estimation of the characteristics of the distribution of values that may occur due to chance associations in the data. The expectation value and standard deviation of this distribution are shown to depend on the autocorrelations and higher order statistics of the data. Where the coherence value falls outside this distribution, we may conclude that there is a causal relationship between the signals regardless of their spectral similarities or differences.

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©2012 American Physical Society