Cortico-Muscular Coherence Enhancement Via Sparse Signal Representation

Yuhang Xu, Qi Yu, Wei Dai, Zoran Cvetkovic, Verity McClelland

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

2 Citations (Scopus)


Identifiction of specific cortico-muscular interactions is essential for understanding sensorimotor control. These interactions are commonly studied by analyzing cortico-muscular coherence (CMC) between electroencephalogram (EEG) and surface electromyogram (sEMG) recorded synchronously under a motor control task. However, the presence of noise and components irrelevant to the monitored task weakens CMC so that it is often very difficult to detect. This study proposes an approach based on dictionary learning and sparse signal representation combined with a component selection algorithm to extract versions of EEG and sEMG signals which contain higher relative levels of coherent components. Evaluations using neurophysiological data show that the method achieves substantial increase in CMC levels.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ISBN (Electronic)978-1-5386-4658-8
ISBN (Print)978-1-5386-4659-5
Publication statusPublished - 13 Sept 2018
Externally publishedYes
EventInternational Conference on Acoustics, Speech and Signal Processing (ICASSP) - Calgary, Canada
Duration: 15 Apr 201820 Apr 2018

Publication series

NameConference Proceedings
ISSN (Electronic)2379-190X


ConferenceInternational Conference on Acoustics, Speech and Signal Processing (ICASSP)
Abbreviated titleICASSP
Internet address


  • Electroencephalography
  • coherence
  • dictionaries
  • sparse matrices
  • task analysis
  • Machine learning
  • convex functions


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