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Inverse modelling to reduce crosstalk in high density surface electromyogram.

Luca Mesin1

  • 1Mathematical Biology and Physiology, Department Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.

Medical Engineering & Physics
|October 21, 2020
PubMed
Summary

Crosstalk in surface electromyography (EMG) hinders accurate muscle analysis. This study introduces an inverse modeling approach to effectively isolate target muscle signals, significantly improving data interpretation and reducing errors in muscle activity assessment.

Keywords:
CrosstalkInverse problemSource localizationSurface EMG

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Kinesiology

Background:

  • Surface electromyography (EMG) signals can be contaminated by crosstalk from adjacent muscles due to their large detection volume.
  • This interference limits the accurate interpretation of target muscle activity, restricting the application of surface EMG.
  • Existing spatial filtering methods can reduce signal representativeness, necessitating alternative solutions.

Purpose of the Study:

  • To develop and validate an inverse modeling approach for estimating individual muscle contributions from surface EMG signals.
  • To effectively isolate the target muscle's activity by removing crosstalk interference.
  • To improve the accuracy of surface EMG data analysis for various applications.

Main Methods:

  • An inverse modeling technique was proposed to differentiate and estimate the contributions of individual muscles from surface EMG.
  • The method was tested using simulated monopolar EMG signals from superficial muscles under varying force levels.
  • Simulations included scenarios with and without model perturbations and noise to assess robustness.

Main Results:

  • The inverse modeling approach significantly reduced the mean squared error in representing the target muscle's EMG from 11.2% to 4.4% (5.3% with noise).
  • Median bias in muscle conduction velocity estimation decreased from 2.12 m/s to 0.72 m/s (1.1 m/s with noise).
  • Median absolute error in median frequency estimation improved from 1.02 Hz to 0.67 Hz (noise-free) and 1.52 Hz to 1.45 Hz (noisy).

Conclusions:

  • The proposed inverse modeling method effectively estimates individual muscle contributions from surface EMG.
  • This approach significantly enhances the accuracy of muscle activity analysis by mitigating crosstalk.
  • The method offers a promising solution for improving the reliability and applicability of surface EMG in research and clinical settings.