TIME-DEPENDENT DUAL-FREQUENCY COHERENCE IN MULTIVARIATE NON-STATIONARY TIME SERIES
TIME-DEPENDENT DUAL-FREQUENCY COHERENCE IN MULTIVARIATE NON-STATIONARY TIME SERIES
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DOI：
10.1111/jtsa.12408
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发表时间：
2019-01-01
影响因子：
0.9
通讯作者：
Von Sachs, Rainer
中科院分区：
文献类型：
--
作者：
Gorrostieta, Cristina;Ombao, Hernando;Von Sachs, Rainer
相干性是多信道信号中的交叉依赖性的一个常见度量。然而，标准相干性并不足以模拟许多具有复杂依赖结构的生物信号，例如一个信号中的低频分量与另一个信号中的高频分量之间的交叉振荡相互作用。低频和高频分量之间的交叉依赖性的概念，如在经典的可调和过程中定义的，仍然是不够的，因为它假设时间不变性，因此不能捕获随时间演变的交叉频率相互作用。我们构建了一个新的框架，用于建模和估计这些依赖关系下的复制时间序列设置。在此框架下，我们建立了新的概念，进化双频相干和发展的时间局部化估计的基础上双频局部周期图。建议的非参数估计程序不遭受模型误设定。它使用局部快速傅立叶变换，因此能够处理大量数据。当应用于运动意图实验中记录的脑电图数据时，所提出的方法揭示了标准方法所忽视的新的和有趣的交叉振荡相互作用。
Coherence is one common metric for cross-dependence in multichannel signals. However, standard coherence does not sufficiently model many biological signals with complex dependence structures such as cross-oscillatory interactions between a low-frequency component in one signal and a high-frequency component in another. The notion of cross-dependence between low- and high-frequency components, as defined in classical harmonizable processes, is still inadequate because it assumes time invariance and thus cannot capture cross-frequency interactions that evolve over time. We construct a novel framework for modeling and estimating these dependencies under the replicated time series setting. Under this framework, we establish the novel concept of evolutionary dual-frequency coherence and develop time-localized estimators based on dual-frequency local periodograms. The proposed nonparametric estimation procedure does not suffer from model misspecification. It uses the localized fast Fourier transform and hence is able to handle massive data. When applied to electroencephalogram data recorded in a motor intention experiment, the proposed method uncovers new and interesting cross-oscillatory interactions that have been overlooked by the standard approaches.