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Applying support vector regression analysis on grip force level-related corticomuscular coherence.

Yao Rong1, Xixuan Han, Dongmei Hao

  • 1College of Life Science and Bioengineering, Beijing University of Technology, Beijing, People's Republic of China, 100124.

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This study explored how grip force affects brain-muscle communication using electroencephalogram (EEG) and electromyogram (EMG). Higher grip force increased corticomuscular coherence in alpha and beta bands, while lower force increased it in the gamma band.

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Area of Science:

  • Neuroscience
  • Motor Control
  • Biomedical Engineering

Background:

  • Voluntary motor performance relies on cortical signals to muscles.
  • Corticomuscular coherence quantifies brain-muscle communication.
  • Accessory muscle control during grip force tasks requires investigation.

Purpose of the Study:

  • To examine the impact of varying grip force levels on corticomuscular coherence.
  • To introduce and validate an algorithm for quantifying brain-muscle coupling.
  • To analyze coherence in specific frequency bands (alpha, beta, gamma) at different grip forces.

Main Methods:

  • Proposed an expanded support vector regression (ESVR) algorithm.
  • Measured electroencephalogram (EEG) from the sensorimotor cortex.
  • Recorded surface electromyogram (EMG) from the brachioradialis muscle.
  • Introduced a 'coherence proportion' measure.
  • Compared coherence at 25% and 75% maximum grip force (MGF).

Main Results:

  • ESVR effectively reduced signal interference and summarized coherence data.
  • Coherence proportion proved more sensitive to grip force changes than coherence area.
  • Significantly higher corticomuscular coherence was observed in alpha and beta bands at 75% MGF (p<0.01).
  • Significantly higher corticomuscular coherence was observed in the gamma band at 25% MGF (p<0.01).

Conclusions:

  • Sensorimotor cortex modulates accessory muscle activity for hand grip via functional corticomuscular coupling.
  • Grip intensity influences corticomuscular coupling differently across alpha, beta, and gamma frequency bands.
  • The findings provide insights into the neural control mechanisms underlying grip force variations.