Multiscale Wavelet Transfer Entropy with Application to Corticomuscular Coupling Analysis

TL;DR

This paper introduces a multiscale wavelet transfer entropy method to detect both linear and non-linear cortico-muscular interactions across multiple frequency bands and time scales, improving sensitivity over traditional techniques.

Contribution

It develops a novel multiscale wavelet transfer entropy approach that captures complex cortico-muscular couplings, including non-linear and cross-frequency interactions, with enhanced sensitivity.

Findings

Effective detection of cortico-muscular information flow in EEG and EMG signals.

Identification of non-linear and cross-frequency interactions.

Alignment with neurophysiological understanding of sensorimotor processes.

Abstract

Objective: Functional coupling between the motor cortex and muscle activity is commonly detected and quantified by cortico-muscular coherence (CMC) or Granger causality (GC) analysis, which are applicable only to linear couplings and are not sufficiently sensitive: some healthy subjects show no significant CMC and GC, and yet have good motor skills. The objective of this work is to develop measures of functional cortico-muscular coupling that have improved sensitivity and are capable of detecting both linear and non-linear interactions. Methods: A multiscale wavelet transfer entropy (TE) methodology is proposed. The methodology relies on a dyadic stationary wavelet transform to decompose electroencephalogram (EEG) and electromyogram (EMG) signals into functional bands of neural oscillations. Then, it applies TE analysis based on a range of embedding delay vectors to detect and quantify intra- and cross-frequency band cortico-muscular coupling at different time scales. Results: Our experiments with neurophysiological signals substantiate the potential of the developed methodologies for detecting and quantifying information flow between EEG and EMG signals for subjects with and without significant CMC or GC, including non-linear cross-frequency interactions, and interactions across different temporal scales. The obtained results are in agreement with the underlying sensorimotor neurophysiology. Conclusion: These findings suggest that the concept of multiscale wavelet TE provides a comprehensive framework for analysing cortex-muscle interactions. Significance: The proposed methodologies will enable developing novel insights into movement control and neurophysiological processes more generally.