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AbstractThis study proposes a method for analysing decomposed electromyographic signals to enhance the characterization of motor units (MU). It involves two steps: (i) clustering groups of motor units based on firing rate (FR), recruitment threshold, and MU action potential amplitude, and (ii) segmentation of data at the time of interest. Such method, capable of distinguishing different groups of MU, could help understanding muscle force production. FR of MU in Vastus Lateralis and Rectus Femoris during knee extension was investigated. The findings distinguish MU groups and reveals differences in: FR between both contractions types; clustered groups; and segmented MU data in both contraction types. [ABSTRACT FROM AUTHOR]

Background: Motor unit (MU) activation during maximal contractions is lower in children compared with adults. Among adults, discrete MU activation differs, depending on the rate of contraction. We investigated the effect of contraction rate on discrete MU activation in boys and men. Methods: Following a habituation session, 14 boys and 20 men completed two experimental sessions for knee extension and wrist flexion, in random order. Maximal voluntary isometric torque (MVIC) was determined before completing trapezoidal isometric contractions (70%MVIC) at low (10%MVIC/s) and high (35%MVIC/s) contraction rates. Surface electromyography was captured from the vastus lateralis (VL) and flexor carpi radialis (FCR) and decomposed into individual MU action potential (MUAP) trains. Results: In both groups and muscles, the initial MU firing rate (MUFR) was greater (p < 0.05) at high compared with low contraction rates. The increase in initial MUFR at the fast contraction in the VL was greater in men than boys (p < 0.05). Mean MUFR was significantly lower during fast contractions only in the FCR (p < 0.05). In both groups and muscles, the rate of decay of MUFR with increasing MUAP amplitude was less steep (p < 0.05) during fast compared with slow contractions. Conclusion: In both groups and muscles, initial MUFRs, as well as MUFRs of large MUs were higher during fast compared with slow contractions. However, in the VL, the increase in initial MUFR was greater in men compared with boys. This suggests that in large muscles, men may rely more on increasing MUFR to generate torque at faster rates compared with boys. [ABSTRACT FROM AUTHOR]

Using global surface electromyography (sEMG) and the sEMG threshold it has been suggested that children activate their type-II motor unit (MU) to a lesser extent compared with adults. However, when age-related differences in discrete MU activation are examined using sEMG decomposition this phenomenon is not observed. Furthermore, findings from these studies are inconsistent and conflicting. Therefore, the purpose of this study was to examine differences in discrete MU activation of the vastus lateralis (VL) between boys and men during moderate-intensity knee extensions. Seventeen boys and 20 men completed two laboratory sessions. Following a habituation session, maximal voluntary isometric knee extension (MVIC) torque was determined before completing trapezoidal contractions at 70% MVIC. sEMG of the VL was captured and mathematically decomposed into individual MU action potential trains. Motor unit action potential amplitude (MUAPamp), recruitment threshold (RT), and MU firing rates (MUFR) were calculated. We observed that MUAPamp–RT slope was steeper in men compared with boys (p < 0.05) even after accounting for fat thickness and quadriceps muscle depth. The mean MUFR and y-intercept of the MUFR–RT relationship were significantly (p < 0.001) lower in boys than in men. The slope of the MUFR–RT relationship tended to be steeper in men, but the differences did not reach statistical significance (p = 0.056). Overall, our results suggest that neural strategies used to produce torque are different among boys and men. Such differences may be related, in part, to boys' lower MUFR and lesser ability to activate their higher-threshold MUs. Although, other factors (e.g., muscle composition) likely also play a role. [ABSTRACT FROM AUTHOR]

Background: Lower activation of higher threshold (type-II) motor units (MUs) has been suggested in children compared with adults. We examined child–adult differences in discrete MU activation of the flexor carpi radialis (FCR). Methods: Fifteen boys (10.2 ± 1.4 years), and 17 men (25.0 ± 2.7 years) completed 2 laboratory sessions. Following a habituation session, maximal voluntary isometric wrist flexion torque (MVIC) was determined before completing trapezoidal isometric contractions at 70%MVIC. Surface electromyography was captured by Delsys Trigno Galileo sensors and decomposed into individual MU action potential trains. Recruitment threshold (RT), and MU firing rates (MUFR) were calculated. Results: MVIC was significantly greater in men (10.19 ± 1.92 Nm) than in boys (4.33 ± 1.47 Nm) (p < 0.05), but not statistically different after accounting for differences in body size. Mean MUFR was not different between boys (17.41 ± 7.83 pps) and men (17.47 ± 7.64 pps). However, the MUFR–RT slope was significantly (p < 0.05) steeper (more negative) in boys, reflecting a progressively greater decrease in MUFR with increasing RT. Additionally, boys recruited more of their MUs early in the ramped contraction. Conclusion: Compared with men, boys tended to recruit their MUs earlier and at a lower percentage of MVIC. This difference in MU recruitment may explain the greater decrease in MUFR with increasing RT in boys compared with men. Overall, these findings suggest an age-related difference in the neural strategy used to develop moderate–high torque in wrist flexors, where boys recruit more of their MUs earlier in the force gradation process, possibly resulting in a narrower recruitment range. [ABSTRACT FROM AUTHOR]

This review aims to describe a novel method in the field of electromyography (EMG), established and improved upon in the last three decades that is able to observe specific parameters of muscle units (MUs). This concept is called the decomposition method, based on its ability to decompose a surface EMG signal to describe muscle activity on the level of individual muscle units in contrast to the level of the whole muscle, as is customary for regular surface electromyography. We provide a brief overview of its history, constituent parts regarding both hardware and software and possible applications. We also acknowledge the state of the research, regarding the background of the decomposition algorithm, the main software component responsible for identifying individual motor units and their parameters. As a result of the ability to describe the behavior of individual motor units during muscle contractions, key concepts in neuromuscular physiology have been put forward, pertaining to the hierarchy of MUs during their recruitment. Together with the recent application for cyclic contractions and gait, the decomposition method is beginning to open up wider possibilities of enquiry. [ABSTRACT FROM AUTHOR]

Background: Post-activation potentiation (PAP) describes the enhancement of twitch torque following a conditioning contraction (CC) in skeletal muscle. In adults, PAP may be related to muscle fibre composition and is accompanied by a decrease in motor unit (MU) firing rates (MUFRs). Muscle fibre composition and/or activation is different between children and adults. This study examined PAP and MU firing patterns of the potentiated knee extensors in boys and men. Methods: Twenty-three boys (10.5 ± 1.3 years) and 20 men (23.1 ± 3.3 years) completed familiarization and experimental sessions. Maximal isometric evoked-twitch torque and MU firing patterns during submaximal contractions (20% and 70% maximal voluntary isometric contraction, MVIC) were recorded before and after a CC (5 s MVIC). PAP was calculated as the percent-increase in evoked-twitch torque after the CC. MU firing patterns were examined during submaximal contractions before and after the CC using Trigno Galileo surface electrodes (Delsys Inc) and decomposition algorithms (NeuroMap, Delsys Inc). MU action potential amplitudes (MUAPamp) and MUFRs were calculated for each MU and exponential MUFR-MUAPamp relationships were calculated for each participant and trial. Results: PAP was higher in men than in boys (98.3 ± 37.1% vs. 68.8 ± 18.3%, respectively; p = 0.002). Following potentiation, the rate of decay of the MUFR-MUAPamps relationship decreased in both contractions, with a greater decrease among boys during the high-intensity contractions. Conclusion: Lower PAP in the boys did not coincide with smaller changes in potentiated MU firing patterns, as boys had greater reductions in MUFRs with potentiation compared with men in high-intensity contractions. [ABSTRACT FROM AUTHOR]

The reliable classification of motor unit action potentials (MUAPs) provides the possibility of tracking motor unit (MU) activities. However, the variation of MUAP profiles caused by multiple factors in realistic conditions challenges the accurate classification of MUAPs. The goal of this study was to propose an effective method based on the convolutional neural network (CNN) to classify MUAPs with high levels of variation for MU tracking. MUAP variation was added artificially in the synthetic electromyogram (EMG) signals and was induced by changing the forearm postures in the experimental study. The proposed overlapped-segment-wise EMG decomposition method and the spike-triggered averaging method were combined to obtain the MUAP waveform samples of individual MUs in the experimental study, and the MUAP profile classification performance was tested. Since the ground-truth of MU discharge activities was known for the synthetic EMG, the MU tracking performance was further verified by mimicking the tracking procedure of MU discharge activities and the spike consistency with the true spike trains was tested in the simulation study. The conventional MUAP similarity index (SI)-based method was also performed as comparison. For both the experimental and the synthetic EMG signals, the CNN-based method significantly improved the MUAP tracking performance compared with the conventional SI-based method manifested as a higher classification accuracy (93.3%±5.4% vs 56.2%±13.9%) in the experimental study or higher spike consistency (71.1%±10.2% vs 29.2%±11.0%) in the simulation study with a smaller variation. These results demonstrated the efficiency and robustness of the proposed method to distinguish MUAPs with large variations accurately. Further development of the proposed method can promote the study on the physiological and pathological changes of the neuromuscular system where tracking MU activities is needed.

The reliable classification of motor unit action potentials (MUAPs) provides the possibility of tracking motor unit (MU) activities. However, the variation of MUAP profiles caused by multiple factors in realistic conditions challenges the accurate classification of MUAPs. The goal of this study was to propose an effective method based on the convolutional neural network (CNN) to classify MUAPs with high levels of variation for MU tracking. MUAP variation was added artificially in the synthetic electromyogram (EMG) signals and was induced by changing the forearm postures in the experimental study. The proposed overlapped-segment-wise EMG decomposition method and the spike-triggered averaging method were combined to obtain the MUAP waveform samples of individual MUs in the experimental study, and the MUAP profile classification performance was tested. Since the ground-truth of MU discharge activities was known for the synthetic EMG, the MU tracking performance was further verified by mimicking the tracking procedure of MU discharge activities and the spike consistency with the true spike trains was tested in the simulation study. The conventional MUAP similarity index (SI)-based method was also performed as comparison. For both the experimental and the synthetic EMG signals, the CNN-based method significantly improved the MUAP tracking performance compared with the conventional SI-based method manifested as a higher classification accuracy (93.3%±5.4% vs 56.2%±13.9%) in the experimental study or higher spike consistency (71.1%±10.2% vs 29.2%±11.0%) in the simulation study with a smaller variation. These results demonstrated the efficiency and robustness of the proposed method to distinguish MUAPs with large variations accurately. Further development of the proposed method can promote the study on the physiological and pathological changes of the neuromuscular system where tracking MU activities is needed. [ABSTRACT FROM AUTHOR]

Objective: Motor unit (MU) discharge timings encode human motor intentions to the finest degree. Whilst tapping into such information can bring significant gains to a range of applications, current approaches to MU decoding from surface signals do not scale well with the demands of dexterous human-machine interfacing (HMI). To optimize the forward estimation accuracy and time-efficiency of such systems, we propose the inclusion of task-wise initialization and MU subset selection. Methods: Offline analyses were conducted on data recorded from 11 non-disabled subjects. Task-wise decomposition was applied to identify MUs from high-density surface electromyography (HD-sEMG) pertaining to 18 wrist/forearm motor tasks. The activities of a selected subset of MUs were extracted from test data and used for forward estimation of intended motor tasks and joint kinematics. To that end, various combinations of subset selection and estimation algorithms (both regression and classification-based) were tested for a range of subset sizes. Results: The mutual information-based minimum Redundancy Maximum Relevance (mRMR-MI) criterion retained MUs with the highest predicative power. When the portion of tracked MUs was reduced down to 25%, the regression performance decreased only by 3% (R2=0.79) while classification accuracy dropped by 2.7% (accuracy = 74%) when kernel-based estimators were considered. Conclusion and Significance: Careful selection of tracked MUs can optimize the efficiency of MU-driven interfacing. In particular, prioritization of MUs exhibiting strong nonlinear relationships with target motions is best leveraged by kernel-based estimators. Hence, this frees resources for more robust and adaptive MU decoding techniques to be implemented in future.

Objective: Motor unit (MU) discharge timings encode human motor intentions to the finest degree. Whilst tapping into such information can bring significant gains to a range of applications, current approaches to MU decoding from surface signals do not scale well with the demands of dexterous human-machine interfacing (HMI). To optimize the forward estimation accuracy and time-efficiency of such systems, we propose the inclusion of task-wise initialization and MU subset selection. Methods: Offline analyses were conducted on data recorded from 11 non-disabled subjects. Task-wise decomposition was applied to identify MUs from high-density surface electromyography (HD-sEMG) pertaining to 18 wrist/forearm motor tasks. The activities of a selected subset of MUs were extracted from test data and used for forward estimation of intended motor tasks and joint kinematics. To that end, various combinations of subset selection and estimation algorithms (both regression and classification-based) were tested for a range of subset sizes. Results: The mutual information-based minimum Redundancy Maximum Relevance (mRMR-MI) criterion retained MUs with the highest predicative power. When the portion of tracked MUs was reduced down to 25%, the regression performance decreased only by 3% (R2=0.79) while classification accuracy dropped by 2.7% (accuracy = 74%) when kernel-based estimators were considered. Conclusion and Significance: Careful selection of tracked MUs can optimize the efficiency of MU-driven interfacing. In particular, prioritization of MUs exhibiting strong nonlinear relationships with target motions is best leveraged by kernel-based estimators. Hence, this frees resources for more robust and adaptive MU decoding techniques to be implemented in future. [ABSTRACT FROM AUTHOR]

A decade ago, a group of researchers from academia and industry identified a dichotomy between the industrial and academic state-of-the-art in upper-limb prosthesis control, a widely used bio-robotics application. They proposed that four key technical challenges, if addressed, could bridge this gap and translate academic research into clinically and commercially viable products. These challenges are unintuitive control schemes, lack of sensory feedback, poor robustness and single sensor modality. Here, we provide a perspective review on the research effort that occurred in the last decade, aiming at addressing these challenges. In addition, we discuss three research areas essential to the recent development in upper-limb prosthetic control research but were not envisioned in the review 10 years ago: deep learning methods, surface electromyogram decomposition and open-source databases. To conclude the review, we provide an outlook into the near future of the research and development in upper-limb prosthetic control and beyond. This is a perspective review of the last ten years of translational research and development efforts in non-invasive neural interfaces and robotics for practical and clinical applications of upper-limb prosthetics. [ABSTRACT FROM AUTHOR]

Maximal rate of force development in adult humans is determined by the maximal motor unit discharge rate, however, the origin of the underlying synaptic inputs remains unclear. Here, we tested a hypothesis that the maximal motor unit discharge rate will increase in response to a startling cue, a stimulus that purportedly activates the pontomedullary reticular formation neurons that make mono- and disynaptic connections to motoneurons via fast-conducting axons. Twenty-two men were required to produce isometric knee extensor forces "as fast and as hard" as possible from rest to 75% of maximal voluntary force, in response to visual (VC), visual-auditory (VAC; 80 dB), or visual-startling cue (VSC; 110 dB). Motoneuron activity was estimated via decomposition of high-density surface electromyogram recordings over the vastus lateralis and medialis muscles. Reaction time was significantly shorter in response to VSC compared with VAC and VC. The VSC further elicited faster neuromechanical responses including a greater number of discharges per motor unit per second and greater maximal rate of force development, with no differences between VAC and VC. We provide evidence, for the first time, that the synaptic input to motoneurons increases in response to a startling cue, suggesting a contribution of subcortical pathways to maximal motoneuron output in humans. [ABSTRACT FROM AUTHOR]

Blind source separation, surface EMG, motor unit, EMG decomposition, performance validation In fact, most surface EMG decomposition algorithms based on blind source separation (BSS) use a parallel search strategy, i.e., they repeatedly search for a number of motor units from the original signal and then deal with duplicates at the end. Keywords: surface EMG; motor unit; EMG decomposition; blind source separation; performance validation EN surface EMG motor unit EMG decomposition blind source separation performance validation 1 4 4 05/30/22 20220526 NES 220526 Introduction Significant progress has been achieved in decomposition of surface electromyographic (EMG) signals, particularly with advances in surface electrode array recording and processing techniques (Holobar and Zazula, [6]; Chen and Zhou, [3]; Negro et al., [11], among others). The agreement in discharge timing of the common motor units from both types of recordings can be viewed as a performance index of the surface EMG decomposition, given that intramuscular EMG decomposition has been well established (Parsaei et al., [12]). [Extracted from the article]

Abstract Background Altered motor unit (MU) activity has been identified after anterior cruciate ligament (ACL) injury, but its effect on muscle tissue properties is unknown. The purpose of this study was to compare thigh musculature muscle stiffness between control and ACL-injured subjects. Methods Thirty ACL-injured subjects and 25 control subjects were recruited. Subjects completed a randomized protocol of isometric contractions while electromyography (EMG) signals were recorded. Three maximum voluntary isometric contractions (MVIC) determined peak force for 10 and 25% MVIC trials. Shear wave elastography was captured during each 10 and 25% MVIC trials. Results Differences in muscle stiffness were assessed between limbs and groups. 12 months post-surgery had higher stiffness for VM 0% MVIC, VL 0 and 10% MVIC, and ST 10 and 25% MVIC (all p ≤ 0.04). Conclusion Thigh musculature stiffness changed throughout rehabilitation and remained altered at 12 months after ACL reconstruction.

Abstract Background Current myoelectric control algorithms for active prostheses map time- and frequency-domain features of the interference EMG signal into prosthesis commands. With this approach, only a fraction of the available information content of the EMG is used and the resulting control fails to satisfy the majority of users. In this study, we predict joint angles of the three degrees of freedom of the wrist from motor unit discharge timings identified by decomposition of high-density surface EMG. Methods We recorded wrist kinematics and high-density surface EMG signals from six able-bodied individuals and one patient with limb deficiency while they performed movements of three degrees of freedom of the wrist at three different speeds. We compared the performance of linear regression to predict the observed individual wrist joint angles from, either traditional time domain features of the interference EMG or from motor unit discharge timings (which we termed neural features) obtained by EMG decomposition. In addition, we propose and test a simple model-based dimensionality reduction, based on the physiological notion that the discharge timings of motor units are partly correlated. Results The regression approach using neural features outperformed regression on classic global EMG features (average R 2 for neural features 0.77 and 0.64, for able-bodied subjects and patients, respectively; for time-domain features 0.70 and 0.52). Conclusions These results indicate that the use of neural information extracted from EMG decomposition can advance man-machine interfacing for prosthesis control.

Appropriate interpretation of motor unit (MU) activities after surface EMG (sEMG) decomposition is a key factor to decode motor intentions in a noninvasive and physiologically meaningful way. However, there are great challenges due to the difficulty in cross-trial MU tracking and unavoidable loss of partial MU information resulting from incomplete decomposition. In light of these issues, this study presents a novel framework for interpreting MU activities and applies it to decode muscle force. The resulting MUs were clustered and classified into different categories by characterizing their spatially distributed firing waveforms. The process served as a general MU tracking method. On this basis, after transferring the MU firing trains to twitch force trains by a twitch force model, a deep network was designed to predict the normalized force. In addition, MU category distribution was examined to calibrate the actual force level, while functions of some unavailable MUs were compensated. To investigate the effectiveness of this framework, high-density sEMG signals were recorded using an $8\times8$ electrode array from the abductor pollicis brevis muscles of eight subjects, while thumb abduction force was measured. The proposed method outperformed three common methods (${p} < {0.001}$) yielding the lowest root mean square deviation of 6.68% ± 1.29% and the highest fitness (${R}^{{2}}$) of 0.94 ± 0.04 between the predicted force and the actual force. This study offers a valuable, computational solution for interpreting individual MU activities, and its effectiveness was confirmed in muscle force estimation. [ABSTRACT FROM AUTHOR]

Background: Altered motor unit (MU) activity has been identified after anterior cruciate ligament (ACL) injury, but its effect on muscle tissue properties is unknown. The purpose of this study was to compare thigh musculature muscle stiffness between control and ACL-injured subjects.Methods: Thirty ACL-injured subjects and 25 control subjects were recruited. Subjects completed a randomized protocol of isometric contractions while electromyography (EMG) signals were recorded. Three maximum voluntary isometric contractions (MVIC) determined peak force for 10 and 25% MVIC trials. Shear wave elastography was captured during each 10 and 25% MVIC trials.Results: Differences in muscle stiffness were assessed between limbs and groups. 12 months post-surgery had higher stiffness for VM 0% MVIC, VL 0 and 10% MVIC, and ST 10 and 25% MVIC (all p ≤ 0.04).Conclusion: Thigh musculature stiffness changed throughout rehabilitation and remained altered at 12 months after ACL reconstruction. [ABSTRACT FROM AUTHOR]

Supplemental digital content is available in the text. Purpose: Motor unit conduction velocity (MUCV) represents the propagation velocity of action potentials along the muscle fibers innervated by individual motor neurons and indirectly reflects the electrophysiological properties of the sarcolemma. In this study, we investigated the effect of a 4-wk strength training intervention on the peripheral properties (MUCV and motor unit action potential amplitude, RMSMU) of populations of longitudinally tracked motor units (MU). Methods: The adjustments exhibited by 12 individuals who participated in the training (INT) were compared with 12 controls (CON). Strength training involved ballistic (4 × 10) and sustained (3 × 10) isometric ankle dorsiflexions. Measurement sessions involved the recordings of maximal voluntary isometric force and submaximal isometric ramp contractions, whereas high-density surface EMG was recorded from the tibialis anterior. High-density surface EMG signals were decomposed into individual MU discharge timings, and MU was tracked across the intervention. Results: Maximal voluntary isometric force (+14.1%, P = 0.003) and average MUCV (+3.0%, P = 0.028) increased in the INT group, whereas normalized MU recruitment threshold (RT) decreased (−14.9%, P = 0.001). The slope (rate of change) of the regression between MUCV and MU RT increased only in the INT group (+32.6%, P = 0.028), indicating a progressive greater increase in MUCV for higher-threshold MU. The intercept (initial value) of MUCV did not change after the intervention (P = 0.568). The association between RMSMU and MU RT was not altered by the training. Conclusion: The increase in the rate of change in MUCV as a function of MU RT, but not the initial value of MUCV, suggests that short-term strength training elicits specific adaptations in the electrophysiological properties of the muscle fiber membrane in high-threshold MU. [ABSTRACT FROM AUTHOR]

Background: Electromyographic (EMG) signal decomposition is the process by which an EMG signal is decomposed into its constituent motor unit potential trains (MUPTs). A major step in EMG decomposition is feature extraction in which each detected motor unit potential (MUP) is represented by a feature vector. As with any other pattern recognition system, feature extraction has a significant impact on the performance of a decomposition system. EMG decomposition has been studied well and several systems were proposed, but feature extraction step has not been investigated in detail. Objective: Several EMG signals were generated using a physiologically-based EMG signal simulation algorithm. For each signal, the firing patterns of motor units (MUs) provided by the simulator were used to extract MUPs of each MU. For feature extraction, different wavelet families including Daubechies (db), Symlets, Coiflets, bi-orthogonal, reverse bi-orthogonal and discrete Meyer were investigated. Moreover, the possibility of reducing the dimensionality of MUP feature vector is explored in this work. The MUPs represented using wavelet-domain features are transformed into a new coordinate system using Principal Component Analysis (PCA). The features were evaluated regarding their capability in discriminating MUPs of individual MUs. Results: Extensive studies on different mother wavelet functions revealed that db2, coif1, sym5, bior2.2, bior4.4, and rbior2.2 are the best ones in differentiating MUPs of different MUs. The best results were achieved at the 4th detail coefficient. Overall, rbior2.2 outperformed all wavelet functions studied; nevertheless for EMG signals composed of more than 12 MUPTs, syms5 wavelet function is the best function. Applying PCA slightly enhanced the results.

Objective: Scapular dyskinesis is one of the causes of shoulder disorders and involves muscle weakness in the serratus anterior. This study investigated whether motor unit (MU) recruitment and firing property, which are important for muscle exertion, have altered in serratus anterior of the individuals with scapular dyskinesis., Methods: Asymptomatic adults with (SD) and without (control) scapular dyskinesis were analyzed. Surface electromyography (sEMG) waveforms were collected at submaximal voluntary contraction of the serratus anterior. The sEMG waveform was decomposed into MU action potential amplitude (MUAP AMP ), mean firing rate (MFR), and recruitment threshold. MUs were divided into low, moderate, and high thresholds, and MU recruitment and firing properties of the groups were compared., Results: High-threshold MUAP AMP was significantly smaller in the SD group than in the control group. The control group also exhibited recruitment properties that reflected the size principle, however, the SD group did not. Furthermore, the SD group had a lower MFR than the control group., Conclusions: Individuals with scapular dyskinesis exhibit altered MU recruitment properties and lower firing rates of the serratus anterior; this may be detrimental to muscle performance. Thus, it may be necessary to improve the neural drive of the serratus anterior when correcting scapular dyskinesis., Competing Interests: The authors have no conflict of interest.

Correlation between motor unit discharge times, often referred to as motor unit synchronization, is determined by common synaptic input to motor neurons. Although it has been largely speculated that synchronization should influence the rate of force development, the association between the degree of motor unit synchronization and rapid force generation has not been determined. In this study, we examined this association with both simulations and experimental motor unit recordings. The analysis of experimental motor unit discharges from the tibialis anterior muscle of 20 healthy individuals during rapid isometric contractions revealed that the average motor unit discharge rate was associated with the rate of force development. Moreover, the extent of motor unit synchronization was entirely determined by the average motor unit discharge rate (R > 0.7, P < 0.0001). The simulation model demonstrated that the relative proportion of common synaptic input received by motor neurons, which determines motor unit synchronization, does not influence the rate of force development (R = 0.03, P > 0.05). Nonetheless, the estimates of correlation between motor unit spike trains were significantly correlated with the rate of force generation (R > 0.8, P < 0.0001). These results indicate that the average motor unit discharge rate, but not the degree of motor unit synchronization, contributes to most of the variance of human contractile speed among individuals. In addition, estimates of correlation between motor unit discharge times depend strongly on the number of identified motor units and therefore are not indicative of the strength of common input. [ABSTRACT FROM AUTHOR]

The aim of this study was to compare voluntary activation (VA) and motor units (MU) recruitment patterns between boys and men at different contraction levels of the knee extensor muscles. We hypothesized that boys and men would display similar VA and MU recruitment patterns at low submaximal force levels, but that boys would display a lower utilization of their higher-threshold MU and a lower VA at near-maximal and maximal force levels than men. 11 prepubertal boys and 13 men were tested at the optimal knee angle. Next, VA was assessed using the twitch interpolation technique during maximal (MVC) and submaximal isometric voluntary contractions. Mean firing rate (MFR), recruitment threshold (RT) and motor unit action potential size (MUAPSIZE) were extracted to characterize neural strategies. No significant difference between groups was found for VA at every contraction level. Similarly, no significant difference was found for the MFR vs. RT relationship parameters between groups. For the vastus lateralis (VL) muscle, the MUAPSIZE vs. RT relationship differed between boys and men independent of the contraction level (p < 0.05). Boys also displayed a different MFR vs. MUAPSIZE relationship on the VL muscle independent of the contraction level (p < 0.05). To conclude, no difference between boys and men was found for VA regardless of the contraction level investigated. Differences in motor unit recruitment parameters between boys and men seem to be explained by different muscle dimensions between groups. [ABSTRACT FROM AUTHOR]

Key points: We propose and validate a method for accurately identifying the activity of populations of motor neurons during contractions at maximal rate of force development in humans.The behaviour of the motor neuron pool during rapid voluntary contractions in humans is presented.We show with this approach that the motor neuron recruitment speed and maximal motor unit discharge rate largely explains the individual ability in generating rapid force contractions.The results also indicate that the synaptic inputs received by the motor neurons before force is generated dictate human potential to generate force rapidly.This is the first characterization of the discharge behaviour of a representative sample of human motor neurons during rapid contractions. During rapid contractions, motor neurons are recruited in a short burst and begin to discharge at high frequencies (up to >200 Hz). In the present study, we investigated the behaviour of relatively large populations of motor neurons during rapid (explosive) contractions in humans, applying a new approach to accurately identify motor neuron activity simultaneous to measuring the rate of force development. The activity of spinal motor neurons was assessed by high‐density electromyographic decomposition from the tibialis anterior muscle of 20 men during isometric explosive contractions. The speed of motor neuron recruitment and the instantaneous motor unit discharge rate were analysed as a function of the impulse (the time–force integral) and the maximal rate of force development. The peak of motor unit discharge rate occurred before force generation and discharge rates decreased thereafter. The maximal motor unit discharge rate was associated with the explosive force variables, at the whole population level (r2 = 0.71 ± 0.12; P < 0.001). Moreover, the peak motor unit discharge and maximal rate of force variables were correlated with an estimate of the supraspinal drive, which was measured as the speed of motor unit recruitment before the generation of afferent feedback (P < 0.05). We show for the first time the full association between the effective neural drive to the muscle and human maximal rate of force development. The results obtained in the present study indicate that the variability in the maximal contractile explosive force of the human tibialis anterior muscle is determined by the neural activation preceding force generation. Key points: We propose and validate a method for accurately identifying the activity of populations of motor neurons during contractions at maximal rate of force development in humans.The behaviour of the motor neuron pool during rapid voluntary contractions in humans is presented.We show with this approach that the motor neuron recruitment speed and maximal motor unit discharge rate largely explains the individual ability in generating rapid force contractions.The results also indicate that the synaptic inputs received by the motor neurons before force is generated dictate human potential to generate force rapidly.This is the first characterization of the discharge behaviour of a representative sample of human motor neurons during rapid contractions. [ABSTRACT FROM AUTHOR]

Low-threshold motor units can be a pain during experimental muscle pain Keywords: EMG decomposition; high-density electromyography; motoneuron; nociception; persistent inward currents EN EMG decomposition high-density electromyography motoneuron nociception persistent inward currents 2545 2547 3 07/03/20 20200701 NES 200701 Neural control of muscle force while experiencing muscle pain is not fully understood yet. GLO:G05/01jul20:tjp14128-fig-0001.jpg PHOTO (COLOR): 1 During muscle contractions, motoneurons receive ionotropic, corticospinal input (purple), increasing the excitability of motoneurons. [Extracted from the article]

Abstract It remains unclear if the sizes of higher-threshold motor units (MU) are associated with muscular strength and power. Therefore, the purpose of this study was to examine sex-related differences in muscle cross-sectional area (mCSA), percent myosin heavy chain (%MHC) isoform expression, and the MU action potential amplitudes (MUAP AMPS)-recruitment threshold (RT) relationships of the vastus lateralis and isometric peak torque, isokinetic peak torque and mean power at 1.05 rad·s−1 of the leg extensors. Surface electromyographic decomposition techniques were used to quantify MUAP AMPS recorded during isometric muscle actions at 70% of maximal voluntary contractions and regressed against RTs with the slopes calculated. Ultrasound images were used to measure mCSA. Males had greater slopes from the MUAP AMP -RT relationship than the females (P < 0.05). The greater slopes likely reflected larger higher-threshold MUs for the males. The mCSAs and slopes from the relationships were strongly correlated with isometric and isokinetic peak torque and isokinetic mean power (r = 0.78–0.82), however, type I %MHC isoform was only moderately correlated with isometric peak torque (r = −0.54). The results indicated that sex-related differences in muscular strength and power were associated more so with the sizes of the higher-threshold MUs (slopes) and mCSA than MHC isoforms. The amount of cross-bridge activity within muscle fibers that comprise higher-threshold MUs may be the primary contributor to muscular strength and power rather than the contractile properties of the muscle. [ABSTRACT FROM AUTHOR]

Feature extraction is an important step of resolving an electromyographic (EMG) signal into its component motor unit potential trains, commonly known as EMG decomposition. Until now, different features have been used to represent motor unit potentials (MUPs) and improve decomposition processing time and accuracy, but a major limitation is that no systematic comparison of these features exists. In an EMG decomposition system, like any pattern recognition system, the features used for representing MUPs play an important role in the overall performance of the system. A cross comparison of the feature extraction methods used in EMG signal decomposition can assist in choosing the best features for representing MUPs and ultimately may improve EMG decomposition results. This paper presents a survey and cross comparison of these feature extraction methods. Decomposability index, classification accuracy of a k -nearest neighbors classifier, and class-feature mutual information were employed for evaluating the discriminative power of various feature extraction techniques commonly used in the literature including time domain, morphological, frequency domain, and discrete wavelets. In terms of data, 45 simulated and 82 real EMG signals were used. Results showed that among time domain features, the first derivative of time samples exhibit the best separability. For morphological features, slope analysis provided the most discriminative power. Discrete Fourier transform coefficients offered the best separability among frequency domain features. However, neither morphological nor frequency domain techniques outperformed time domain features. The detail 4 coefficients in a discrete wavelets decomposition exceeded in evaluation measures when compared with other feature extraction techniques. Using principal component analysis slightly improved the results, but it is time consuming. Overall, considering computation time and discriminative ability, the first derivative of time samples might be efficient in representing MUPs in EMG decomposition and there is no need for sophisticated feature extraction methods. [ABSTRACT FROM PUBLISHER]

A novel electromyography (EMG) signal decomposition framework is presented for the thorough and precise analysis of intramuscular EMG signals. This framework first detects all of the active motor unit action potentials (MUAPs) and assigns single MUAP segments to their corresponding motor units. MUAP waveforms that are found to be superimposed are then resolved into their constituent single MUAPs using a peel-off approach and similarly assigned. The method is composed of six stages of analytical procedures: preprocessing, segmentation, alignment and feature extraction, clustering and refinement, supervised classification, and superimposed waveform resolution. The performance of the proposed decomposition framework was evaluated using both synthetic EMG signals and real recordings obtained from healthy and stroke participants. The overall detection rate of MUAPs was 100% for both synthetic and real signals. The average accuracy for synthetic EMG signals was 87.23%. Average assignment accuracies of 88.63 and 94.45% were achieved for the real EMG signals obtained from healthy and stroke participants, respectively. Results demonstrated the ability of the developed framework to decompose intramuscular EMG signals with improved accuracy and efficiency, which we believe will greatly benefit the clinical utility of EMG for the diagnosis and rehabilitation of motor impairments in stroke patients.

A novel electromyography (EMG) signal decomposition framework is presented for the thorough and precise analysis of intramuscular EMG signals. This framework first detects all of the active motor unit action potentials (MUAPs) and assigns single MUAP segments to their corresponding motor units. MUAP waveforms that are found to be superimposed are then resolved into their constituent single MUAPs using a peel-off approach and similarly assigned. The method is composed of six stages of analytical procedures: preprocessing, segmentation, alignment and feature extraction, clustering and refinement, supervised classification, and superimposed waveform resolution. The performance of the proposed decomposition framework was evaluated using both synthetic EMG signals and real recordings obtained from healthy and stroke participants. The overall detection rate of MUAPs was 100% for both synthetic and real signals. The average accuracy for synthetic EMG signals was 87.23%. Average assignment accuracies of 88.63 and 94.45% were achieved for the real EMG signals obtained from healthy and stroke participants, respectively. Results demonstrated the ability of the developed framework to decompose intramuscular EMG signals with improved accuracy and efficiency, which we believe will greatly benefit the clinical utility of EMG for the diagnosis and rehabilitation of motor impairments in stroke patients. [ABSTRACT FROM AUTHOR]

We prove the feasibility of decomposing high density surface EMG signals from forearm muscles in non-isometric wrist motor tasks of normally limbed and limb-deficient individuals with the perspective of using the decoded neural information for prosthesis control. For this purpose, we recorded surface EMG signals during motions of three degrees of freedom of the wrist in seven normally limbed subjects and two patients with limb deficiency. The signals were decomposed into individual motor unit activity with a convolutive blind source separation algorithm. On average, for each subject, 16 ± 7 motor units were identified per motor task. The discharge timings of these motor units were estimated with an accuracy > 85%. Moreover, the activity of 6 ± 5 motor units per motor task was consistently detected in all repetitions of the same task. The joint angle at which motor units were first identified was 62.5 ± 26.4% of the range of motion, indicating a prevalence in the identification of high threshold motor units. These findings prove the feasibility of accurate identification of the neural drive to muscles in contractions relevant for myoelectric control, allowing the development of a new generation of myocontrol methods based on motor unit spike trains. [ABSTRACT FROM PUBLISHER]

Background: Electromyographic (EMG) signal decomposition is the process by which an EMG signal is decomposed into its constituent motor unit potential trains (MUPTs). A major step in EMG decomposition is feature extraction in which each detected motor unit potential (MUP) is represented by a feature vector. As with any other pattern recognition system, feature extraction has a significant impact on the performance of a decomposition system. EMG decomposition has been studied well and several systems were proposed, but feature extraction step has not been investigated in detail. Objective: Several EMG signals were generated using a physiologically-based EMG signal simulation algorithm. For each signal, the firing patterns of motor units (MUs) provided by the simulator were used to extract MUPs of each MU. For feature extraction, different wavelet families including Daubechies (db), Symlets, Coiflets, bi-orthogonal, reverse bi-orthogonal and discrete Meyer were investigated. Moreover, the possibility of reducing the dimensionality of MUP feature vector is explored in this work. The MUPs represented using wavelet-domain features are transformed into a new coordinate system using Principal Component Analysis (PCA). The features were evaluated regarding their capability in discriminating MUPs of individual MUs. Results: Extensive studies on different mother wavelet functions revealed that db2, coif1, sym5, bior2.2, bior4.4, and rbior2.2 are the best ones in differentiating MUPs of different MUs. The best results were achieved at the 4th detail coefficient. Overall, rbior2.2 outperformed all wavelet functions studied; nevertheless for EMG signals composed of more than 12 MUPTs, syms5 wavelet function is the best function. Applying PCA slightly enhanced the results. [ABSTRACT FROM AUTHOR]

The neural command from motor neurons to muscles - sometimes referred to as the neural drive to muscle - can be identified by decomposition of electromyographic (EMG) signals. This approach can be used for inferring the voluntary commands in neural interfaces in patients with limb amputations. This paper proposes for the first time an innovative method for fully automatic and real-time intramuscular EMG (iEMG) decomposition. The method is based on online single-pass density-based clustering and adaptive classification of bivariate features, using the concept of potential measure. No attempt was made to resolve superimposed motor unit action potentials. The proposed algorithm was validated on sets of simulated and experimental iEMG signals. Signals were recorded from the biceps femoris long-head, vastus medialis and lateralis and tibialis anterior muscles during low-to-moderate isometric constant-force and linearly-varying force contractions. The average number of missed, duplicated and erroneous clusters for the examined signals was , , and , respectively. The average decomposition accuracy (defined similar to signal detection theory but without using True Negatives in the denominator) and coefficient of determination (variance accounted for) for the cumulative discharge rate estimation were , and , respectively. The time cost for processing each 200ms iEMG interval was (21-97)ms. However, computational time generally increases over time as a function of frames/signal epochs. Meanwhile, the incremental accuracy defined as the accuracy of real-time analysis of each signal epoch, was % for epochs recorded after initial one second. The proposed algorithm is thus a promising new tool for neural decoding in the next-generation of prosthetic control. [ABSTRACT FROM AUTHOR]

Introduction: The purpose of this study was to investigate the effects of eccentric exercise on common drive. Methods: Eleven men, age 23.6 ± 2.1 (mean ± SD) years, performed trapezoid isometric muscle actions of the dominant forearm flexors immediately before and after 1 of 2 interventions: (a) 6 sets of 10 maximal eccentric isokinetic muscle actions of the forearm flexors; or (b) 10 minutes of quiet resting. Surface electromyographic signals were recorded from the biceps brachii and decomposed into individual motor unit action potential trains. Mean firing rate patterns were calculated for each motor unit, and all possible combinations were cross-correlated to measure common drive. Results: The peak cross-correlation coefficients were generally in the 0.2-0.5 range and occasionally as high as 0.7. Conclusion: These coefficients were not, however, affected by the eccentric exercise, despite a 19.5% decrease in strength, indicating that the eccentric exercise did not affect common drive. Muscle Nerve, 2012 [ABSTRACT FROM AUTHOR]

We studied the innervation and organization of motor units in the brachioradialis muscle of 25 normal human subjects. We recorded intramuscular EMG signals at points separated by 15 mm along the proximodistal muscle axis during moderate isometric contractions, identified from 27 to 61 (mean 39) individual motor units per subject using EMG decomposition, and estimated the locations of the endplates and distal muscle/tendon junctions from the motor-unit action potential (MUAP) propagation patterns and terminal standing waves. In three subjects all the motor units were innervated in a single endplate zone. In the other 22 subjects, the motor units were innervated in 3-6 (mean 4) distinct endplate zones separated by 15-55 mm along the proximodistal axis. One-third of the motor units had fibers innervated in more than one zone. The more distally innervated motor units had distinct terminal waves indicating tendonous termination, while the more proximal motor units lacked terminal waves, indicating intrafascicular termination. Analysis of blocked MUAP components revealed that 19% of the motor units had at least one doubly innervated fiber, i.e., a fiber innervated in two different endplate zones by two different motoneurons, and thus belonging to two different motor units. These results are consistent with the brachioradialis muscle having a series-fibered architecture consisting of multiple, overlapping bands of muscle fibers in most individuals and a simple parallel-fibered architecture in some individuals. [ABSTRACT FROM AUTHOR]

Objective . Motor unit (MU) discharge information obtained via the online electromyogram (EMG) decomposition has shown promising prospects in multiple applications. However, the nonstationarity of EMG signals caused by the rotation (recruitment-derecruitment) of MUs and the variation of MU action potentials (MUAP) can significantly degrade the online decomposition performance. This study aimed to develop an independent component analysis-based online decomposition method that can accommodate the nonstationarity of EMG signals. Approach . The EMG nonstationarity can make the separation vectors obtained beforehand inaccurate, resulting in the reduced amplitudes of the peaks corresponding to firing events in the source signal (independent component) and then the decreased accuracy of firing events. Therefore, we utilized the FitzHugh-Nagumo (FHN) resonance model to enhance the firing peaks in the source signal in order to improve the decomposition accuracy. A two-session approach was used with the offline session to extract the separation vectors and train the FHN models. In the online session, the source signal was estimated and further processed using the FHN model before detecting the firing events in a real-time manner. The proposed method was tested on simulated EMG signals, in which MU rotation and MUAP variation were involved to mimic the nonstationarity of EMG recordings. Main results . Compared with the conventional method, the proposed method can improve the decomposition accuracy significantly (88.70% ± 4.17% vs. 92.43% ± 2.79%) by enhancing the firing peaks, and more importantly, the improvement was more prominent when the EMG signal had stronger background noises (87.00% ± 3.70% vs. 91.66% ± 2.63%). Conclusions . Our results demonstrated the effectiveness of the proposed method to utilize the FHN model to improve the online decomposition performance on the nonstationary EMG signals. Further development of our method has the potential to improve the performance of the neural decoding system that utilizes the MU discharge information and promote its application in the neural-machine interface., (© 2022 IOP Publishing Ltd.)

The article focuses on the properties of Motor control of motor units active during rapid contractions. Topics discussed include motor unit activation properties considered as primary mechanisms in the modulation of force production; mention the findings of study that suggest the maximal rate of force development (RFD); and information on the two early and late separate phases of the muscle contraction.