Motor unit recruitment cannot be inferred from surface EMG amplitude and basic reporting standards must be adhered to

2015 ◽  
Vol 116 (3) ◽  
pp. 657-658 ◽  
Author(s):  
Andrew D. Vigotsky ◽  
Dan Ogborn ◽  
Stuart M. Phillips
2004 ◽  
Vol 92 (5) ◽  
pp. 2878-2886 ◽  
Author(s):  
Ping Zhou ◽  
William Zev Rymer

The dependence of the form of the EMG-force relation on key motoneuron and muscle properties was explored using a simulation approach. Surface EMG signals and isometric forces were simulated using existing motoneuron pool, muscle force, and surface EMG models, based primarily on reported properties of the first dorsal interosseous (FDI) muscle in humans. Our simulation results indicate that the relation between electrical and mechanical properties of the individual motor unit level plays the dominant role in determining the overall EMG amplitude-force relation of the muscle, while the underlying motor unit firing rate strategy appears to be a less important factor. However, different motor unit firing rate strategies result in substantially different relations between counts of the numbers of motoneuron discharges and the isometric force. Our simulation results also show that EMG amplitude (estimated as the average rectified value) increases as a result of synchronous discharges of different motor units within the pool, but the magnitude of this increase is determined primarily by the action potential duration of the synchronized motor units. Furthermore, when the EMG effects are normalized to their maximum levels, motor unit synchrony does not exert significant effects on the form of the EMG-force relation, provided that the synchrony level is held similar at different excitation levels.


2017 ◽  
Vol 123 (4) ◽  
pp. 835-843 ◽  
Author(s):  
Alessandro Del Vecchio ◽  
Francesco Negro ◽  
Francesco Felici ◽  
Dario Farina

The surface interference EMG signal provides some information on the neural drive to muscles. However, the association between neural drive to muscle and muscle activation has long been debated with controversial indications due to the unavailability of motor unit population data. In this study, we clarify the potential and limitations of interference EMG analysis to infer motor unit recruitment strategies with an experimental investigation of several concurrently active motor units and of the associated features of the surface EMG. For this purpose, we recorded high-density surface EMG signals during linearly increasing force contractions of the tibialis anterior muscle, up to 70% of maximal force. The recruitment threshold (RT), conduction velocity (MUCV), median frequency (MDFMU), and amplitude (RMSMU) of action potentials of 587 motor units from 13 individuals were assessed and associated with features of the interference EMG. MUCV was positively associated with RT ( R2 = 0.64 ± 0.14), whereas MDFMU and RMSMU showed a weaker relation with RT ( R2 = 0.11 ± 0.11 and 0.39 ± 0.24, respectively). Moreover, the changes in average conduction velocity estimated from the interference EMG predicted well the changes in MUCV ( R2 = 0.71), with a strong association to ankle dorsiflexion force ( R2 = 0.81 ± 0.12). Conversely, both the average EMG MDF and RMS were poorly associated with motor unit recruitment. These results clarify the limitations of EMG spectral and amplitude analysis in inferring the neural strategies of muscle control and indicate that, conversely, the average conduction velocity could provide relevant information on these strategies. NEW & NOTEWORTHY The surface EMG provides information on the neural drive to muscles. However, the associations between EMG features and neural drive have been long debated due to unavailability of motor unit population data. Here, by using novel highly accurate decomposition of the EMG, we related motor unit population behavior to a wide range of voluntary forces. The results fully clarify the potential and limitation of the surface EMG to provide estimates of the neural drive to muscles.


2015 ◽  
Vol 115 (11) ◽  
pp. 2407-2414 ◽  
Author(s):  
Li-Ling Pan ◽  
Chung-Huang Yu ◽  
Mei-Wun Tsai ◽  
Shun-Hwa Wei ◽  
Li-Wei Chou

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