A Time-frequency Based Multivariate Phase-amplitude Coupling Measure

T. T. Munia, Selin Aviyente
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引用次数: 3

Abstract

Interaction of neuronal oscillations across different frequency bands plays an important role in perception, attention, and memory. One particular form of interaction is the modulation of the amplitude of high-frequency oscillations by the phase of low-frequency oscillations, known as phase-amplitude coupling (PAC). Current methods for quantifying PAC mostly rely on Hilbert transform which assumes that brain activity is stationary and narrowband. Moreover, these methods are limited to quantifying bivariate PAC and cannot capture multivariate cross-frequency coupling between different brain regions. This paper presents a new complex time-frequency based high resolution PAC measure and its extension to the multivariate case using PARAFAC (Parallel Factor) model. The proposed approach is evaluated on both simulated and real electroencephalogram (EEG) data.
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一种基于时频的多变量相幅耦合测量方法
不同频带的神经元振荡相互作用在感知、注意和记忆中起着重要作用。相互作用的一种特殊形式是通过低频振荡的相位调制高频振荡的振幅,称为相位振幅耦合(PAC)。目前量化PAC的方法主要依赖希尔伯特变换,希尔伯特变换假设大脑活动是固定的和窄带的。此外,这些方法仅限于量化二元PAC,无法捕获不同脑区之间的多元交叉频率耦合。本文提出了一种新的基于复时频的高分辨率PAC测量方法,并将其推广到多变量情况。在模拟和真实的脑电图数据上对该方法进行了评估。
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