DCS Corporation, Alexandria, VA 22310, USA.
J Neurosci Methods. 2013 Jan 30;212(2):247-58. doi: 10.1016/j.jneumeth.2012.10.002. Epub 2012 Oct 18.
Detecting significant periods of phase synchronization in EEG recordings is a non-trivial task that is made especially difficult when considering the effects of volume conduction and common sources. In addition, EEG signals are often confounded by non-neural signals, such as artifacts arising from muscle activity or external electrical devices. A variety of phase synchronization analysis methods have been developed with each offering a different approach for dealing with these confounds. We investigate the use of a parametric estimation of the time-frequency transform as a means of improving the detection capability for a range of phase analysis methods. We argue that such an approach offers numerous benefits over using standard nonparametric approaches. We then demonstrate the utility of our technique using both simulated and actual EEG data by showing that the derived phase synchronization estimates are more robust to noise and volume conduction effects.
检测 EEG 记录中的显著相位同步时间段是一项艰巨的任务，尤其是在考虑容积传导和共同源的影响时。此外，EEG 信号常常受到非神经信号的干扰，例如来自肌肉活动或外部电子设备的伪影。已经开发了各种相位同步分析方法，每种方法都提供了一种不同的方法来处理这些干扰。我们研究了使用时频变换的参数估计作为提高一系列相位分析方法检测能力的一种手段。我们认为，与使用标准非参数方法相比，这种方法具有许多优势。然后，我们通过显示衍生的相位同步估计对噪声和容积传导效应更具鲁棒性，使用模拟和实际 EEG 数据证明了我们技术的实用性。
