Multiscale Wavelet Transfer Entropy With Application to Corticomuscular Coupling Analysis

Zhenghao Guo, Verity M. Mcclelland, Osvaldo Simeone, Kerry R. Mills, Zoran Cvetkovic

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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 analyzing cortex-muscle interactions. Significance: The proposed methodologies will enable developing novel insights into movement control and neurophysiological processes more generally.

Original languageEnglish (US)
Pages (from-to)771-782
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Issue number2
StatePublished - Feb 1 2022

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering


  • Corticomuscular coupling
  • cross-frequency coupling
  • information transfer
  • multiscale
  • wavelets analysis


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