Je. Kasprisin et Md. Grabiner, EMG VARIABILITY DURING MAXIMUM VOLUNTARY ISOMETRIC AND ANISOMETRIC CONTRACTIONS IS REDUCED USING SPATIAL AVERAGING, Journal of electromyography and kinesiology, 8(1), 1998, pp. 45-50
Electromyography (EMG) is a commonly used tool that can be plagued wit
h poor signal-to-noise ratios. One result of poor signal-to-noise rati
os is increased within-and between-subject variability of quantified E
MG variables, for example, the integrated EMG. Methods that reduce wit
hin-and between-subject variability of quantified EMG variables can in
crease the statistical power of an experimental design and aid in the
functional interpretation of experimental results. The purpose of this
investigation was to determine the effectiveness of spatially averagi
ng the surface EMG signal to reduce the variability of the quantified
EMG obtained during maximum voluntary contractions (MVC). The present
study extends the work of earlier investigators describing the enhance
d signal characteristics obtained by spatially averaging the surface E
MG measured during submaximum voluntary isometric contractions and str
etch reflexes. Ten subjects performed maximum voluntary isometric and
anisometric (concentric and eccentric) contractions of the elbow flexo
rs. Four electrodes, forming two pairs of bipolar electrodes were plac
ed over both the biceps brachii and brachioradialis muscles. Four rect
ified and integrated EMG signals from the electrode array were compare
d. Data from each subject's contraction condition and from each muscle
were used to compute a coefficient of variation that was considered r
epresentative of the within-subject variability. These data were analy
sed with a multifactorial repeated measures analysis of variance (ANOV
A). The results revealed a muscle-specific, statistically significant
superiority of one of the methods in reducing the variability of the r
ectified and integrated EMG signal. Summing the rectified and integrat
ed signals from each bipolar pair of electrodes in the array was shown
to reduce significantly the within-subject variability. (C) 1998 Else
vier Science Ltd. All rights reserved.