EEG super-resolution with Laplacian Regularized Coupled Matrix Decomposition: A case study of Autism Spectrum Disorder EEG enhancement.

Abstract

EEG Super-resolution (SR) has attracted increasing attention for neuroscience research when fine-grained spatial information is demanding. However, existing SR methods are subject to the performance bottlenecks due to insufficient high-resolution EEG under the condition of few participants undergoing high-density EEG acquisition and unclear intrinsic spatiotemporal relationship amongst EEG channels on the scalp. To tackle the issues, this study proposes a Laplacian Regularized Coupled Matrix Decomposition (LRCMD) model for EEG SR, which takes in HR EEG of a small amount of initial participants and generates HR EEG from the given LR counterparts of new participants: (1) LRCMD first utilizes a Gaussian kernel function according to the spatial distribution of electrodes conforming to a 3-D scalp model to measure the brain structural connectivity amongst EEG channels, (2) Coupled matrix decomposition model is established to transform the HR EEG and the corresponding LR ones to latent source space with common mapping rule, where brain structural connectivity acts as Laplacian regularization to highlight the core mapping rule, (3) LRCMD applies Alternating Direction Method of Multipliers solver to cope with the decomposition model and derive the mapping matrix along with latent source of HR EEG, which are later leveraged to complete the SR reconstruction of LR EEG from new participants. Experimental results on ASD EEG dataset indicate that (1) LRCMD excels in individual EEG super-resolution reconstruction with normalized mean squared error decreased by 2.14% and the improvements of signal-to-noise ratio, Pearson's correlation coefficient respectively reaching 0.52 dB, 1.17%, and (2) the reconstructed EEG by LRCMD demonstrates superiority to LR alternative in ASD discrimination and functional connectivity analysis of ASD.