Learning Cortico-Muscular Dependence through Orthonormal Decomposition of Density Ratios

TL;DR

This paper introduces a novel statistical method using orthonormal decomposition of density ratios to analyze cortico-muscular dependence, improving interpretability and scalability in EEG-EMG connectivity studies.

Contribution

The study develops a new approach based on eigenvalues and eigenfunctions to model cortico-muscular relationships, addressing limitations of existing methods.

Findings

Eigenfunctions classify movements and subjects accurately.

Method reveals channel and temporal dependencies during movement.

Code implementation is publicly available.

Abstract

The cortico-spinal neural pathway is fundamental for motor control and movement execution, and in humans it is typically studied using concurrent electroencephalography (EEG) and electromyography (EMG) recordings. However, current approaches for capturing high-level and contextual connectivity between these recordings have important limitations. Here, we present a novel application of statistical dependence estimators based on orthonormal decomposition of density ratios to model the relationship between cortical and muscle oscillations. Our method extends from traditional scalar-valued measures by learning eigenvalues, eigenfunctions, and projection spaces of density ratios from realizations of the signal, addressing the interpretability, scalability, and local temporal dependence of cortico-muscular connectivity. We experimentally demonstrate that eigenfunctions learned from cortico-muscular connectivity can accurately classify movements and subjects. Moreover, they reveal channel and temporal dependencies that confirm the activation of specific EEG channels during movement. Our code is available at https://github.com/bohu615/corticomuscular-eigen-encoder.