» Articles » PMID: 39462400

The Probability Density Function of the Surface Electromyogram and Its Dependence on Contraction Force in the Vastus Lateralis

Overview
Publisher Biomed Central
Date 2024 Oct 27
PMID 39462400
Authors
Affiliations
Soon will be listed here.
Abstract

Introduction: The probability density function (PDF) of the surface electromyogram (sEMG) depends on contraction force. This dependence, however, has so far been investigated by having the subject generate force at a few fixed percentages of MVC. Here, we examined how the shape of the sEMG PDF changes with contraction force when this force was gradually increased from zero.

Methods: Voluntary surface EMG signals were recorded from the vastus lateralis of healthy subjects as force was increased in a continuous manner vs. in a step-wise fashion. The sEMG filling process was examined by measuring the EMG filling factor, computed from the non-central moments of the rectified sEMG signal.

Results: (1) In 84% of the subjects, as contraction force increased from 0 to 10% MVC, the sEMG PDF shape oscillated back and forth between the semi-degenerate and the Gaussian distribution. (2) The PDF-force relation varied greatly among subjects for forces between 0 and ~ 10% MVC, but this variability was largely reduced for forces above 10% MVC. (3) The pooled analysis showed that, as contraction force gradually increased, the sEMG PDF evolved rapidly from the semi-degenerate towards the Laplacian distribution from 0 to 5% MVC, and then more slowly from the Laplacian towards the Gaussian distribution for higher forces.

Conclusions: The study demonstrated that the dependence of the sEMG PDF shape on contraction force can only be reliably assessed by gradually increasing force from zero, and not by performing a few constant-force contractions. The study also showed that the PDF-force relation differed greatly among individuals for contraction forces below 10% MVC, but this variability was largely reduced when force increased above 10% MVC.

References
1.
Sanger T . Bayesian filtering of myoelectric signals. J Neurophysiol. 2006; 97(2):1839-45. DOI: 10.1152/jn.00936.2006. View

2.
Rodriguez-Falces J, Malanda A, Mariscal C, Navallas J . The filling factor of the sEMG signal at low contraction forces in the quadriceps muscles is influenced by the thickness of the subcutaneous layer. Front Physiol. 2024; 14:1298317. PMC: 10796493. DOI: 10.3389/fphys.2023.1298317. View

3.
Navallas J, Eciolaza A, Mariscal C, Malanda A, Rodriguez-Falces J . EMG probability density function: a new way to look at EMG signal filling from single motor unit potential to full interference pattern. IEEE Trans Neural Syst Rehabil Eng. 2023; PP. DOI: 10.1109/TNSRE.2023.3241354. View

4.
Hunter I, Kearney R, Jones L . Estimation of the conduction velocity of muscle action potentials using phase and impulse response function techniques. Med Biol Eng Comput. 1987; 25(2):121-6. DOI: 10.1007/BF02442838. View

5.
Clancy E, Hogan N . Probability density of the surface electromyogram and its relation to amplitude detectors. IEEE Trans Biomed Eng. 1999; 46(6):730-9. DOI: 10.1109/10.764949. View