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Combining Multiple Data Acquisition Systems to Study Corticospinal Output and Multi-segment Biomechanics

Combining Multiple Data Acquisition Systems to Study Corticospinal Output and Multi-segment Biomechanics

Published on: January 9, 2016

Corticomuscular coupling analysis based on improved LSTM and transfer entropy.

Fei Ye¹, Ziyang Sun², Donghui Yang²

¹Jinhua Municipal Central Hospital, Jinhua 321000, China; Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua 321000, China.

Neuroscience Letters

|June 7, 2021

Summary

This study introduces a novel model to detect corticomuscular coupling strength, enhancing brain-muscle interaction analysis. The model accurately captures bidirectional coupling and reduces false positives, aiding rehabilitation and movement decoding.

Keywords:

Corticomuscular coupling Electroencephalogram Electromyography Transfer entropy

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Corticospinal Excitability Modulation During Action Observation

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Published on: December 31, 2013

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Functional corticomuscular coupling reveals brain-muscle interaction dynamics.
Understanding this coupling is crucial for brain control of muscles and movement's effect on brain function.
Existing methods may struggle with accurately quantifying complex corticomuscular interactions.

Purpose of the Study:

To propose and validate a novel detection model for quantifying corticomuscular coupling strength.
To analyze the coupling relationship between the cerebral cortex and muscles during specific movements.
To compare the proposed model's performance against traditional and deep learning-based methods.

Main Methods:

Utilized an adaptive selector to identify the optimal Long Short-Term Memory (LSTM) network.
Extracted features from electroencephalography (EEG) and electromyography (EMG) signals using the selected LSTM.
Transformed time-domain features to the frequency domain and employed transfer entropy to quantify signal interaction intensity across frequency bands.

Main Results:

The proposed model effectively expressed bidirectional coupling between different frequency bands.
Demonstrated superior performance in suppressing false coupling compared to wavelet coherence and deep canonical correlation analysis.
Successfully analyzed corticomuscular coupling during wrist flexion, wrist extension, and clenching fist movements.

Conclusions:

The developed model offers a robust method for assessing corticomuscular coupling.
The model's ability to accurately detect bidirectional interactions and mitigate false positives holds significant potential.
Applications include medical rehabilitation, movement decoding, and advancing our understanding of neuromechanical control.