Michigan State University, Department of Electrical and Computer Engineering, East Lansing, MI- 48824, USA.
Sci Rep. 2019 Aug 27;9(1):12441. doi: 10.1038/s41598-019-48870-2.
Oscillatory activity in the brain has been associated with a wide variety of cognitive processes including decision making, feedback processing, and working memory. The high temporal resolution provided by electroencephalography (EEG) enables the study of variation of oscillatory power and coupling across time. Various forms of neural synchrony across frequency bands have been suggested as the mechanism underlying neural binding. Recently, a considerable amount of work has focused on phase-amplitude coupling (PAC)- a form of cross-frequency coupling where the amplitude of a high frequency signal is modulated by the phase of low frequency oscillations. The existing methods for assessing PAC have some limitations including limited frequency resolution and sensitivity to noise, data length and sampling rate due to the inherent dependence on bandpass filtering. In this paper, we propose a new time-frequency based PAC (t-f PAC) measure that can address these issues. The proposed method relies on a complex time-frequency distribution, known as the Reduced Interference Distribution (RID)-Rihaczek distribution, to estimate both the phase and the envelope of low and high frequency oscillations, respectively. As such, it does not rely on bandpass filtering and possesses some of the desirable properties of time-frequency distributions such as high frequency resolution. The proposed technique is first evaluated for simulated data and then applied to an EEG speeded reaction task dataset. The results illustrate that the proposed time-frequency based PAC is more robust to varying signal parameters and provides a more accurate measure of coupling strength.
大脑中的振荡活动与各种认知过程有关，包括决策、反馈处理和工作记忆。脑电图 (EEG) 提供的高时间分辨率使我们能够研究振荡功率和耦合随时间的变化。各种形式的跨频带神经同步被认为是神经绑定的机制。最近，大量的工作集中在相位-幅度耦合 (PAC) 上——一种跨频耦合形式，其中高频信号的幅度由低频振荡的相位调制。现有的 PAC 评估方法存在一些局限性，包括由于对带通滤波的固有依赖，频率分辨率有限、对噪声敏感、数据长度和采样率有限。在本文中，我们提出了一种新的基于时频的 PAC(t-fPAC)测量方法，可以解决这些问题。该方法依赖于一种复时频分布，称为简化干扰分布 (RID)-Rihaczek 分布，分别估计低频和高频振荡的相位和包络。因此，它不依赖于带通滤波，并且具有时频分布的一些理想特性，例如高频分辨率。该技术首先在模拟数据上进行评估，然后应用于 EEG 快速反应任务数据集。结果表明，所提出的基于时频的 PAC 对信号参数的变化更具鲁棒性，并且提供了更准确的耦合强度测量。
