Testing for time-localized coherence in bivariate data
Testing for time-localized coherence in bivariate data
复制标题
DOI：
10.1103/physreve.85.046205
复制
发表时间：
2012-04-09
影响因子：
2.4
通讯作者：
McClintock, P. V. E.
中科院分区：
文献类型：
--
作者：
Sheppard, L. W.;Stefanovska, A.;McClintock, P. V. E.
我们提出了一种检验显著性的方法，当评估两个振荡时间序列的相干性时，可能具有可变的幅度和频率。它是基于评估时间序列的自相关性。我们通过将基于小波的相干测量应用于人工和生理实例来证明我们的方法。由于这类相干度量受到时间序列的光谱特征的强烈偏倚，我们通过估计由于数据中的偶然关联而可能出现的值的分布特征来评估显著性。该分布的期望值和标准差取决于数据的自相关性和高阶统计量。当相干值落在这个分布之外时，我们可以得出结论，信号之间存在因果关系，而不管它们的光谱相似性或差异性。
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.