Facial muscle activation patterns in healthy male humans: a multi-channel surface EMG study

J Neurosci Methods. 2010 Mar 15;187(1):120-8. doi: 10.1016/j.jneumeth.2009.12.019. Epub 2010 Jan 11.

Abstract

In order to accurately characterize essential muscle activity during facial movements a new surface EMG (SEMG) technique was introduced and applied. Results represent reference data of healthy persons for future diagnostic purposes. In 30 healthy males monopolar electromyograms of the facial muscles were simultaneously recorded from 48 bilateral-symmetrically applied small surface electrodes while performing 29 facial movements of high clinical relevance. Mean SEMG amplitudes were quantified by power spectral analysis, normalized and presented as movement-related SEMG profiles. The mean SEMG amplitudes increased significantly in response to facial movements. Critical values of the movement-related SEMG amplitude increase were ascertained, valid for 90% of all examined subjects. The mean SEMG amplitudes differed between the performed facial movements, the examined muscles, and intramuscularly between lateral-medial and superior-inferior electrode positions, but not systematically between right and left side of face. The results show that the interplay between individual facial muscles and intramuscularly between their functional subunits is more differentiated than was previously estimated. With the presented facial SEMG technique the produced SEMG profiles are highly relevant for better planning of facial movement restoration. Based on the established reference data, this method can be used to objectively evaluate a facial paresis and to monitor changes during the course of disease and treatment. To easily apply the method, a reduction of electrode positions is intended after the clinical evaluation.

MeSH terms

  • Adult
  • Electromyography / instrumentation*
  • Electromyography / methods*
  • Evoked Potentials, Motor
  • Facial Muscles / physiology*
  • Functional Laterality
  • Health Status
  • Humans
  • Male
  • Movement / physiology*
  • Signal Processing, Computer-Assisted