Cross-frequency coupling is emerging as a crucial mechanism that coordinates the integration of spectrally and spatially distributed neuronal oscillations. Recently, phase-amplitude coupling, a form of cross-frequency coupling, where the phase of a slow oscillation modulates the amplitude of a fast oscillation, has gained attention. Existing phase-amplitude coupling measures are mostly confined to either coupling within a region or between pairs of brain regions. Given the availability of multi-channel electroencephalography recordings, a multivariate analysis of phase amplitude coupling is needed to accurately quantify the coupling across multiple frequencies and brain regions. In the present work, we propose a tensor based approach, i.e., higher order robust principal component analysis, to identify response-evoked phase-amplitude coupling across multiple frequency bands and brain regions. Our experiments on both simulated and electroencephalography data demonstrate that the proposed multivariate phase-amplitude coupling method can capture the spatial and spectral dynamics of phase-amplitude coupling more accurately compared to existing methods. Accordingly, we posit that the proposed higher order robust principal component analysis based approach filters out the background phase-amplitude coupling activity and predominantly captures the event-related phase-amplitude coupling dynamics to provide insight into the spatially distributed brain networks across different frequency bands.

中文翻译：

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使用张量稳健 PCA 对 EEG 数据中的双变量相位-幅度耦合进行多变量分析

交叉频率耦合正在成为协调光谱和空间分布的神经元振荡整合的关键机制。最近，相位-幅度耦合，一种交叉频率耦合的形式，其中慢振荡的相位调制快速振荡的幅度，引起了人们的关注。现有的相位-幅度耦合测量大多局限于一个区域内或成对的大脑区域之间的耦合。鉴于多通道脑电图记录的可用性，需要对相位幅度耦合进行多变量分析，以准确量化跨多个频率和大脑区域的耦合。在目前的工作中，我们提出了一种基于张量的方法，即高阶稳健主成分分析，识别跨多个频带和大脑区域的响应诱发的相位-幅度耦合。我们对模拟和脑电图数据的实验表明，与现有方法相比，所提出的多元相位幅度耦合方法可以更准确地捕捉相位幅度耦合的空间和频谱动态。因此，我们假设所提出的基于高阶稳健主成分分析的方法滤除背景相位-幅度耦合活动，并主要捕获与事件相关的相位-幅度耦合动力学，以提供对跨不同频带的空间分布大脑网络的洞察。我们对模拟和脑电图数据的实验表明，与现有方法相比，所提出的多元相位幅度耦合方法可以更准确地捕捉相位幅度耦合的空间和频谱动态。因此，我们假设所提出的基于高阶稳健主成分分析的方法滤除背景相位-幅度耦合活动，并主要捕获与事件相关的相位-幅度耦合动力学，以提供对跨不同频带的空间分布大脑网络的洞察。我们对模拟和脑电图数据的实验表明，与现有方法相比，所提出的多元相位幅度耦合方法可以更准确地捕捉相位幅度耦合的空间和频谱动态。因此，我们假设所提出的基于高阶稳健主成分分析的方法滤除背景相位-幅度耦合活动，并主要捕获与事件相关的相位-幅度耦合动力学，以提供对跨不同频带的空间分布大脑网络的洞察。