Estimating Time-Evolving Partial Coherence Between Signals via Multivariate Locally Stationary Wavelet Processes
Estimating Time-Evolving Partial Coherence Between Signals via Multivariate Locally Stationary Wavelet Processes
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DOI：
10.1109/tsp.2014.2343937
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发表时间：
2014-10-15
影响因子：
5.4
通讯作者：
Ombao, Hernando C.
中科院分区：
文献类型：
--
作者：
Park, Timothy;Eckley, Idris A.;Ombao, Hernando C.
我们考虑的问题，估计时间局部交叉依赖的非平稳信号的集合。为此，我们开发了多变量局部平稳小波框架，它提供了一个时间尺度分解的信号，因此，自然地捕捉到时间演变的尺度特定的交叉依赖的信号分量之间。在该模型下，我们严格定义和估计了两种形式的交叉依赖测度：小波相干性和小波偏相干性。这些依赖性度量方法有一个微妙但重要的区别。前者是对依赖性的广泛衡量，可能包括间接关联，即，由另一个信号驱动的一对信号之间的依赖性。相反，小波部分相干性测量一对信号之间的直接线性关联，即，它消除了其它观测信号的线性效应。因此，我们的时间尺度小波部分相干估计方案提供了一种机制，用于识别网络内的非平稳信号的隐藏的动态关系，我们证明在视觉运动实验中记录的脑电图。
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.