Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment
Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment
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DOI：
10.1007/s004229900137
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发表时间：
2000-07-01
影响因子：
1.9
通讯作者：
Liang, HL
中科院分区：
文献类型：
--
作者：
Ding, MZ;Bressler, SL;Liang, HL
在这篇文章中，我们考虑应用参数谱分析多通道事件相关电位（ERP）在认知实验。我们表明，适当的数据预处理，自适应多变量自回归（AMVAR）建模是一种有效的技术，用于处理非平稳ERP时间序列。我们提出了一个自助程序来评估估计的光谱量的变化。最后，我们将AMVAR谱分析应用于视觉整合任务，揭示了在任务处理的不同阶段快速变化的皮层动力学。
In this article we consider the application of parametric spectral analysis to multichannel event-related potentials (ERPs) during cognitive experiments. We show that with proper data preprocessing, Adaptive MultiVariate AutoRegressive (AMVAR) modeling is an effective technique for dealing with nonstationary ERP time series. We propose a bootstrap procedure to assess the variability in the estimated spectral quantities. Finally, we apply AMVAR spectral analysis to a visuomotor integration task, revealing rapidly changing cortical dynamics during different stages of task processing.