Estimating time-evolving partial coherence between signals via multivariate locally stationary wavelet processes

Park, Timothy Alexander and Eckley, Idris and Ombao, Hernando (2014) Estimating time-evolving partial coherence between signals via multivariate locally stationary wavelet processes. IEEE Transactions on Signal Processing, 62 (20). 5240 - 5250. ISSN 1053-587X

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Abstract

We consider the problem of estimating time-localized cross-dependence in a collection of nonstationary signals. To this end, we develop the multivariate locally stationary wavelet framework, which provides a time-scale decomposition of the signals and, thus, naturally captures the time-evolving scale-specific cross-dependence between components of the signals. Under the proposed model, we rigorously define and estimate two forms of cross-dependence measures: wavelet coherence and wavelet partial coherence. These dependence measures differ in a subtle but important way. The former is a broad measure of dependence, which may include indirect associations, i.e., dependence between a pair of signals that is driven by another signal. Conversely, wavelet partial coherence measures direct linear association between a pair of signals, i.e., it removes the linear effect of other observed signals. Our time-scale wavelet partial coherence estimation scheme thus provides a mechanism for identifying hidden dynamic relationships within a network of nonstationary signals, as we demonstrate on electroencephalograms recorded in a visual–motor experiment.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Signal Processing
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Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2208
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ID Code:
70987
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Deposited On:
26 Sep 2014 08:14
Refereed?:
Yes
Published?:
Published
Last Modified:
23 Oct 2020 02:35