Assessment of across-muscle coherence using multi-unit vs. single-unit recordings.
Coherence between electromyographic (EMG) signals has been used to identify correlated neural inputs to motor units (MUs) innervating different muscles. Simulations using a motor-unit model (Fuglevand et al. 1992) were performed to determine the ability of coherence between two multi-unit EMGs (mEMG) to detect correlated MU activity and the range of correlation strengths in which mEMG coherence can be usefully employed. Coherence between motor-unit and mEMG activities in two muscles was determined as we varied the strength of a 30-Hz periodic common input, the number of correlated MU pairs and variability of MU discharge relative to the common input. Pooled and mEMG coherence amplitudes positively and negatively accelerated, respectively, toward the strongest and most widespread correlating inputs. Furthermore, the relation between pooled and mEMG coherence was also nonlinear and was essentially the same whether correlation strength varied by changing common input strength or its distribution. However, the most important finding is that while the mEMG coherence saturates at the strongest common input strengths, this occurs at common input strengths greater than found in most physiological studies. Thus, we conclude that mEMG coherence would be a useful measure in many experimental conditions and our simulation results suggest further guidelines for using and interpreting coherence between mEMG signals.
Faculty of Kinesiology, University of Calgary, Calgary, AB, Canada.
Jamie A
JA
Johnston
Gabriele
G
Formicone
Thomas M
TM
Hamm
Marco
M
Santello
D000200:Action Potentials / Q000502:physiology*; D004576:Electromyography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D009046:Motor Neurons / Q000502:physiology*; D009068:Movement / Q000502:physiology; D009119:Muscle Contraction / Q000502:physiology; D018482:Muscle, Skeletal / Q000294:innervation / Q000502:physiology*; D009469:Neuromuscular Junction / Q000502:physiology*; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted