Delay differential analysis of electroencephalographic data.
Delay differential analysis of electroencephalographic data.
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DOI：
10.1162/neco_a_00656
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发表时间：
2015-03
影响因子：
2.9
通讯作者：
Sejnowski TJ
中科院分区：
文献类型：
--
作者：
Lainscsek C;Hernandez ME;Poizner H;Sejnowski TJ
我们提出了一种时域方法来检测频率，频率耦合，和相位使用非线性相关函数。对于频率分析，这种方法是离散傅立叶变换的多元扩展，对于高阶谱，它是多维相关的多维快速傅立叶变换的线性和多元替代。该方法可以应用于短和稀疏的时间序列，并可以扩展到交叉试验和交叉通道频谱（CTS）的脑电图数据，其中多个短的数据段，从多个试验相同的实验是可用的。CTS有两个版本。第一个假设一些相位相干性跨越试验，而第二个是独立的相位相干性。我们表明，相位依赖的版本是更符合事件相关的谱微扰分析和传统的Morlet小波分析。我们表明，CTS可以适用于短的数据窗口，并产生更高的时间分辨率比传统的Morlet小波分析。此外，CTS可以用于使用CTS的所有线性分量来重建事件相关电位。
We propose a time-domain approach to detect frequencies, frequency couplings, and phases using nonlinear correlation functions. For frequency analysis, this approach is a multivariate extension of discrete Fourier transform, and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short and sparse time series and can be extended to cross-trial and cross-channel spectra (CTS) for electroencephalography data where multiple short data segments from multiple trials of the same experiment are available. There are two versions of CTS. The first one assumes some phase coherency across the trials, while the second one is independent of phase coherency. We demonstrate that the phase-dependent version is more consistent with event-related spectral perturbation analysis and traditional Morlet wavelet analysis. We show that CTS can be applied to short data windows and yields higher temporal resolution than traditional Morlet wavelet analysis. Furthermore, the CTS can be used to reconstruct the event-related potential using all linear components of the CTS.