A Reliable Myoelectric Signal Detector Based on the Propagation Characteristics of Motor Unit Action Potentials

1986 ◽  
Vol BME-33 (9) ◽  
pp. 876-878
Author(s):  
Tadashi Masuda
Keyword(s):  
Motor Unit ◽  
Action Potentials ◽  
Propagation Characteristics ◽  
Myoelectric Signal ◽  
Signal Detector ◽  
Motor Unit Action Potentials ◽  
Motor Unit Action ◽  
Unit Action

Sex‐related differences in muscle size explained by amplitudes of higher‐threshold motor unit action potentials and muscle fibre typing

2018 ◽  
Vol 225 (4) ◽  
pp. e13151 ◽  
Author(s):  
M. A. Trevino ◽  
A. J. Sterczala ◽  
J. D. Miller ◽  
M. E. Wray ◽  
H. L. Dimmick ◽  
...  
Keyword(s):  
Motor Unit ◽  
Muscle Fibre ◽  
Action Potentials ◽  
Muscle Size ◽  
Motor Unit Action Potentials ◽  
Motor Unit Action ◽  
Fibre Typing ◽  
Unit Action

Age effects on properties of motor unit action potentials: ADEMG analysis

1988 ◽  
Vol 24 (2) ◽  
pp. 207-213 ◽  
Author(s):  
Januarye E. Howard ◽  
Kevin C. McGill ◽  
Leslie J. Dorfman
Keyword(s):  
Motor Unit ◽  
Action Potentials ◽  
Age Effects ◽  
Motor Unit Action Potentials ◽  
Motor Unit Action ◽  
Unit Action

scholarly journals The extraction of neural strategies from the surface EMG: an update

2014 ◽  
Vol 117 (11) ◽  
pp. 1215-1230 ◽  
Author(s):  
Dario Farina ◽  
Roberto Merletti ◽  
Roger M. Enoka
Keyword(s):  
Motor Unit ◽  
Action Potentials ◽  
Surface Emg ◽  
Neural Code ◽  
Neural Activation ◽  
Emg Signal ◽  
Motor Unit Action Potentials ◽  
Motor Unit Action ◽  
Unit Action ◽  
Surface Emg Signal

A surface EMG signal represents the linear transformation of motor neuron discharge times by the compound action potentials of the innervated muscle fibers and is often used as a source of information about neural activation of muscle. However, retrieving the embedded neural code from a surface EMG signal is extremely challenging. Most studies use indirect approaches in which selected features of the signal are interpreted as indicating certain characteristics of the neural code. These indirect associations are constrained by limitations that have been detailed previously (Farina D, Merletti R, Enoka RM. J Appl Physiol 96: 1486–1495, 2004) and are generally difficult to overcome. In an update on these issues, the current review extends the discussion to EMG-based coherence methods for assessing neural connectivity. We focus first on EMG amplitude cancellation, which intrinsically limits the association between EMG amplitude and the intensity of the neural activation and then discuss the limitations of coherence methods (EEG-EMG, EMG-EMG) as a way to assess the strength of the transmission of synaptic inputs into trains of motor unit action potentials. The debated influence of rectification on EMG spectral analysis and coherence measures is also discussed. Alternatively, there have been a number of attempts to identify the neural information directly by decomposing surface EMG signals into the discharge times of motor unit action potentials. The application of this approach is extremely powerful, but validation remains a central issue.

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