Detecting the unique representation of motor-unit action potentials in the surface electromyogram.
This study investigated the relative proportion of motor-unit action potentials that are uniquely represented in the simulated and experimental surface electromyogram (EMG). Two hundred motor units were simulated in a cylindrical anatomical system. Action potentials for each motor unit were generated with a model and then compared with those of other motor units. Pairs of motor units were considered indistinguishable and the motor units not uniquely represented in the surface EMG, when the difference in the mean energy for the pair of potentials was <5%. The anatomical conditions and recording configurations had a substantial influence on the percentage of motor units that could be uniquely identified in the simulated EMG. For example, a single monopolar channel could discriminate only 3.4% of motor units in the simulated population, whereas a system with 81 Laplacian channels arranged in a grid could discriminate 83.8% of the motor units under the same conditions. The simulation results were confirmed with populations of motor units recorded experimentally from the abductor digiti minimi muscle of eight healthy men. Furthermore, the relative proportion of uniquely identified motor units in the simulated signal was only moderately related to motor-unit size and distance from the electrodes. These results indicate the upper limit for detection of individual motor units from the surface EMG and show that a few channels of surface EMG recordings are not sufficient to study single motor units. The noninvasive identification of motor units from the surface EMG requires the use of multiple channels of information.
Farina
Dario
D
Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
Negro
Francesco
F
Gazzoni
Marco
M
Enoka
Roger M
RM
D000200:Action Potentials / Q000502:physiology*; D003198:Computer Simulation; D004558:Electric Stimulation; D004576:Electromyography; D006801:Humans; D008959:Models, Neurological; D009046:Motor Neurons / Q000502:physiology*; D009119:Muscle Contraction; D018482:Muscle, Skeletal / Q000166:cytology*; D009431:Neural Conduction; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted