Wavelet Canonical Coherence for Nonstationary Signals
Haibo Wu, Marina I. Knight, Keiland W. Cooper, Norbert J. Fortin, Hernando Ombao

TL;DR
This paper introduces WaveCanCoh, a wavelet-based method for analyzing time-varying, frequency-specific dependencies between nonstationary multivariate signals, with applications in neuroscience.
Contribution
It extends canonical coherence analysis to nonstationary signals using wavelets, enabling scale-specific, time-varying dependence estimation between signal clusters.
Findings
Accurately recovers true coherence structures in simulations.
Detects dynamic neural coherence patterns related to behavior.
Provides interpretable insights into nonstationary multivariate interactions.
Abstract
Understanding the evolving dependence between two clusters of multivariate signals is fundamental in neuroscience and other domains where sub-networks in a system interact dynamically over time. Despite the growing interest in multivariate time series analysis, existing methods for between-clusters dependence typically rely on the assumption of stationarity and lack the temporal resolution to capture transient, frequency-specific interactions. To overcome this limitation, we propose scale-specific wavelet canonical coherence (WaveCanCoh), a novel framework that extends canonical coherence analysis to the nonstationary setting by leveraging the multivariate locally stationary wavelet model. The proposed WaveCanCoh enables the estimation of time-varying canonical coherence between clusters, providing interpretable insight into scale-specific time-varying interactions between clusters.…
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Taxonomy
TopicsImage and Signal Denoising Methods
