Coherence of the Surface EMG and Common Synaptic Input to Motor Neurons
Coherence between electromyographic (EMG) signals is often used to infer the common synaptic input to populations of motor neurons. This analysis, however, may be limited due to the filtering effect of the motor unit action potential waveforms. This study investigated the ability of surface EMG–EMG...
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doaj-e0a9751c593b4f398dbc827c6c10ff322020-11-25T02:39:17ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612018-06-011210.3389/fnhum.2018.00207329175Coherence of the Surface EMG and Common Synaptic Input to Motor NeuronsJakob L. Dideriksen0Francesco Negro1Deborah Falla2Signe R. Kristensen3Natalie Mrachacz-Kersting4Dario Farina5Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, Aalborg, DenmarkDepartment of Clinical and Experimental Sciences, Università degli Studi di Brescia, Brescia, ItalyCentre of Precision Rehabilitation for Spinal Pain, School of Sport, Exercise, and Rehabilitation Sciences, College of Life and Environmental Sciences, University of Birmingham, Birmingham, United KingdomCenter for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, Aalborg, DenmarkCenter for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, Aalborg, DenmarkDepartment of Bioengineering, Imperial College London, London, United KingdomCoherence between electromyographic (EMG) signals is often used to infer the common synaptic input to populations of motor neurons. This analysis, however, may be limited due to the filtering effect of the motor unit action potential waveforms. This study investigated the ability of surface EMG–EMG coherence to predict common synaptic input to motor neurons. Surface and intramuscular EMG were recorded from two locations of the tibialis anterior muscle during steady ankle dorsiflexions at 5 and 10% of the maximal force in 10 healthy individuals. The intramuscular EMG signals were decomposed to identify single motor unit spike trains. For each trial, the strength of the common input in different frequency bands was estimated from the coherence between two cumulative spike trains, generated from sets of single motor unit spike trains (reference measure). These coherence values were compared with those obtained from the coherence between the surface EMG signals (raw, rectified, and high-passed filtered at 250 Hz before rectification) using linear regression. Overall, the high-pass filtering of the EMG prior to rectification did not substantially change the results with respect to rectification only. For both signals, the correlation of EMG coherence with motor unit coherence was strong at 5% MVC (r2 > 0.8; p < 0.01), but only for frequencies > 5 Hz. At 10% MVC, the correlation between EMG and motor unit coherence was only significant for frequencies > 15 Hz (r2 > 0.8; p < 0.01). However, when using raw EMG for coherence analysis, the only significant relation with motor unit coherence was observed for the bandwidth 5–15 Hz (r2 > 0.68; p = 0.04). In all cases, there was no association between motor unit and EMG coherence for frequencies < 5 Hz (r2 ≤ 0.2; p ≥ 0.51). In addition, a substantial error in the best linear fit between motor unit and EMG coherence was always present. In conclusion, high-frequency (>5 Hz) common synaptic inputs to motor neurons can partly be estimated from the rectified surface EMG at low-level steady contractions. The results, however, suggest that this association is weakened with increasing contraction intensity and that input at lower frequencies during steady isometric contractions cannot be detected accurately by surface EMG coherence.https://www.frontiersin.org/article/10.3389/fnhum.2018.00207/fullcoherencemotor controlmotor unitsurface EMGsynaptic input |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jakob L. Dideriksen Francesco Negro Deborah Falla Signe R. Kristensen Natalie Mrachacz-Kersting Dario Farina |
spellingShingle |
Jakob L. Dideriksen Francesco Negro Deborah Falla Signe R. Kristensen Natalie Mrachacz-Kersting Dario Farina Coherence of the Surface EMG and Common Synaptic Input to Motor Neurons Frontiers in Human Neuroscience coherence motor control motor unit surface EMG synaptic input |
author_facet |
Jakob L. Dideriksen Francesco Negro Deborah Falla Signe R. Kristensen Natalie Mrachacz-Kersting Dario Farina |
author_sort |
Jakob L. Dideriksen |
title |
Coherence of the Surface EMG and Common Synaptic Input to Motor Neurons |
title_short |
Coherence of the Surface EMG and Common Synaptic Input to Motor Neurons |
title_full |
Coherence of the Surface EMG and Common Synaptic Input to Motor Neurons |
title_fullStr |
Coherence of the Surface EMG and Common Synaptic Input to Motor Neurons |
title_full_unstemmed |
Coherence of the Surface EMG and Common Synaptic Input to Motor Neurons |
title_sort |
coherence of the surface emg and common synaptic input to motor neurons |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Human Neuroscience |
issn |
1662-5161 |
publishDate |
2018-06-01 |
description |
Coherence between electromyographic (EMG) signals is often used to infer the common synaptic input to populations of motor neurons. This analysis, however, may be limited due to the filtering effect of the motor unit action potential waveforms. This study investigated the ability of surface EMG–EMG coherence to predict common synaptic input to motor neurons. Surface and intramuscular EMG were recorded from two locations of the tibialis anterior muscle during steady ankle dorsiflexions at 5 and 10% of the maximal force in 10 healthy individuals. The intramuscular EMG signals were decomposed to identify single motor unit spike trains. For each trial, the strength of the common input in different frequency bands was estimated from the coherence between two cumulative spike trains, generated from sets of single motor unit spike trains (reference measure). These coherence values were compared with those obtained from the coherence between the surface EMG signals (raw, rectified, and high-passed filtered at 250 Hz before rectification) using linear regression. Overall, the high-pass filtering of the EMG prior to rectification did not substantially change the results with respect to rectification only. For both signals, the correlation of EMG coherence with motor unit coherence was strong at 5% MVC (r2 > 0.8; p < 0.01), but only for frequencies > 5 Hz. At 10% MVC, the correlation between EMG and motor unit coherence was only significant for frequencies > 15 Hz (r2 > 0.8; p < 0.01). However, when using raw EMG for coherence analysis, the only significant relation with motor unit coherence was observed for the bandwidth 5–15 Hz (r2 > 0.68; p = 0.04). In all cases, there was no association between motor unit and EMG coherence for frequencies < 5 Hz (r2 ≤ 0.2; p ≥ 0.51). In addition, a substantial error in the best linear fit between motor unit and EMG coherence was always present. In conclusion, high-frequency (>5 Hz) common synaptic inputs to motor neurons can partly be estimated from the rectified surface EMG at low-level steady contractions. The results, however, suggest that this association is weakened with increasing contraction intensity and that input at lower frequencies during steady isometric contractions cannot be detected accurately by surface EMG coherence. |
topic |
coherence motor control motor unit surface EMG synaptic input |
url |
https://www.frontiersin.org/article/10.3389/fnhum.2018.00207/full |
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