Multivariate Analysis of Bivariate Phase-Amplitude Coupling in EEG Data Using Tensor Robust PCA.

Tamanna T K Munia,Selin Aviyente

doi:10.1109/tnsre.2021.3092890

Abstract

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.

Highlights

N EURONAL oscillations and the interactions between them are believed to play a key role in understanding the cognitive function of the brain [1], [2]
The high performance exhibited by the Higher order Robust Principal Component Analysis (HoRPCA) and PARAFAC based methods indicates that the tensor based multi-way analysis takes full advantage of the multi-linear structure of the data, which improves the classification performance compared to matrix factorization-based methods like gedCFC
PARAFAC is unable to detect the correct coupled channel pairs in the presence of variability across subjects. This can be explained by the fact that PARAFAC based method detects the channel pairs which are common across all subjects

Summary

Introduction

N EURONAL oscillations and the interactions between them are believed to play a key role in understanding the cognitive function of the brain [1], [2]. The interaction and coordination between oscillations at different frequencies. RID-Rihaczek time-frequency distribution of a signal x(t) is defined as1 [20]: C(t, f ) = (θ τ ) exp − σ × exp θτ j. This is a complex-valued distribution that can be employed to extract the amplitude and phase components of a given signal.

Methods

Results

Discussion

Conclusion