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Analysis of corticomuscular connectivity during walking using vine copula
Xiebing Chen1,2, Yuliang Ma1,2, Xiaoyun Liu1,2
1School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
Mathematical Biosciences and Engineering : MBE
|July 2, 2021
Summary
This study introduces a new vine copula method to model brain (EEG) and muscle (sEMG) signals, revealing corticomuscular connectivity during lower limb movements.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Systems Biology
Background:
- Corticomuscular connectivity is crucial for controlling human movement.
- Understanding the neural pathways involved in locomotion is essential.
Purpose of the Study:
- To develop a novel method for jointly modeling electroencephalography (EEG) and surface electromyography (sEMG) signals.
- To investigate corticomuscular connectivity during lower limb movements like walking.
Main Methods:
- Recording EEG and sEMG signals during level ground and stair walking tasks.
- Applying a novel vine copula-based approach to model signal dependencies.
- Constructing a corticomuscular function network to analyze information transmission.
Main Results:
- The vine copula method successfully modeled joint EEG and sEMG signals.
- A corticomuscular function network revealed information flow between cortex, muscles, and between them.
- Effective connectivity values quantified significant changes in the network during lower limb movements.
Conclusions:
- Vine copula is a powerful tool for analyzing complex corticomuscular interactions.
- This approach offers a quantitative method to assess neural control of movement.
- The findings advance our understanding of brain-muscle communication during locomotion.
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