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Recent studies combining high-density surface electromyography (HD-sEMG) and ultrasound imaging have yielded valuable insights into the relationship between motor unit activity and muscle contractile properties. However, limited evidence exists on the relationship between motor unit firing properties and tendon morpho-mechanical properties. This study aimed to determine the relationship between triceps surae motor unit firing properties and the morpho-mechanical properties of the Achilles tendon (AT). Motor unit firing properties [i.e. mean discharge rate (DR) and coefficient of variation of the interspike interval (COVisi)] and motor unit firing-torque relationships [cross-correlation between cumulative spike train (CST) and torque, and the delay between motor unit firing and torque production (neuromechanical delay)] of the medial gastrocnemius (MG), lateral gastrocnemius (LG), and soleus (SO) muscles were assessed using HD-sEMG during isometric plantarflexion contractions at 10% and 40% of maximal voluntary contraction (MVC). The morpho-mechanical properties of the AT (i.e. length, thickness, cross-sectional area, and resting stiffness) were determined using B-mode ultrasonography and shear-wave elastography. Multiple linear regression analysis showed that at 10% MVC, the DR of the triceps surae muscles explained 41.7% of the variance in resting AT stiffness. In addition, at 10% MVC, COV isi SO predicted 30.4% of the variance in AT length. At 40% MVC, COV isi MG and COV isi SO explained 48.7% of the variance in AT length. Motor unit-torque relationships were not associated with any morpho-mechanical parameter. This study provides novel evidence of a contraction intensity-dependent relationship between motor unit firing parameters of the triceps surae muscle and the morpho-mechanical properties of the AT. NEW & NOTEWORTHY By employing HD-sEMG, conventional B-mode ultrasonography, and shear-wave elastography, we showed that the resting stiffness of the Achilles tendon is related to mean discharge rate of triceps surae motor units during low-force isometric plantarflexion contractions, providing relevant information about the complex interaction between rate coding and the muscle-tendon unit.

The acquisition of a motor skill involves adaptations of spinal and supraspinal pathways to alpha motoneurons. In this study, we estimated the shared synaptic contributions of these pathways to understand the neural mechanisms underlying the short-term acquisition of a new force-matching task. High-density surface electromyography (HDsEMG) was acquired from the first dorsal interosseous (FDI; 7 males and 6 females) and tibialis anterior (TA; 7 males and 4 females) during 15 trials of an isometric force-matching task. For two selected trials (pre- and post-skill acquisition), we decomposed the HDsEMG into motor unit spike trains, tracked motor units between trials, and calculated the mean discharge rate and the coefficient of variation of interspike interval (COV ISI ). We also quantified the post/pre ratio of motor units' coherence within delta, alpha, and beta bands. Force-matching improvements were accompanied by increased mean discharge rate and decreased COV ISI for both muscles. Moreover, the area under the curve within alpha band decreased by ∼22% (TA) and ∼13% (FDI), with no delta or beta bands changes. These reductions correlated significantly with increased coupling between force/neural drive and target oscillations. These results suggest that short-term force-matching skill acquisition is mediated by attenuation of physiological tremor oscillations in the shared synaptic inputs. Supported by simulations, a plausible mechanism for alpha band reductions may involve spinal interneuron phase-cancelling descending oscillations. Therefore, during skill learning, the central nervous system acts as a matched filter, adjusting synaptic weights of shared inputs to suppress neural components unrelated to the specific task., Competing Interests: The authors declare no competing financial interests., (Copyright © 2024 Cabral et al.)

The aim of our study was to compare the force steadiness and the discharge characteristics of motor units in the tibialis anterior (TA) during ankle dorsiflexion and foot adduction produced by submaximal isometric contractions with the dominant and non-dominant foot. Fifteen young men performed maximal and submaximal contractions at five target forces with both legs, and motor unit activity in TA was recorded using high-density electromyography. Maximal force and the fluctuations in force during submaximal contractions were similar between the two legs ( p  > 0.05). Motor unit activity was characterized by measures of mean discharge rate (MDR), coefficient of variation for interspike interval (CoV for ISI), and standard deviation of the filtered cumulative spike train (SD of fCST). There were no statistically significant differences in motor unit activity between legs during ankle dorsiflexion. In contrast, the MDR and the CoV for ISI but not the SD of fCST, were greater for the non-dominant foot compared with the dominant foot during foot adduction. Nonetheless, these differences in motor unit activity were not sufficient to influence the force fluctuations during the submaximal contractions. These results indicate that control of the force produced by TA during the two actions was not influenced by limb dominance.

The pectoralis major assists in several shoulder movements, such as humeral vertical and horizontal adduction, flexion, extension, and internal rotation. Despite its involvement in numerous functional activities, its role in typical shoulder function is ambiguous. Due to this, its purpose in arm movement is largely diminished. However, mounting evidence associates pectoralis major injuries to long-term debilitating arm disability. Therefore, a more deliberate investigation of its role in typical shoulder function is paramount. The purpose of this paper is to outline the current limitations in the acquisition and characterization of pectoralis major activation using standard bipolar surface electromyography. Macroscopic level analyses are used to investigate pectoralis major activation in eight tasks at low (15-25% of maximal voluntary effort (MVE) and moderate (50% MVE) efforts in healthy males. Virtually derived bipolar EMG amplitudes are quantified for the clavicular and the upper sternocostal regions based on the common locations used to acquire EMG signals from classic EMG. HD-sEMG amplitudes from three pectoralis major regions (i.e. clavicular, upper, and lower sternocostal) were compared to virtually derived bipolar EMG amplitudes (i.e. clavicular and upper sternocostal) to determine if current EMG methods misestimate pectoralis major activity. Current findings indicate that classic EMG recordings mischaracterize pectoralis major activation in several tasks and effort levels, highlighting the importance of acquiring signals from multiple pectoralis major regions., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Objective: Noninvasive estimation of motoneuron excitability in human motoneurons is achieved through a paired motor unit analysis (ΔF) that quantifies hysteresis in the instantaneous firing rates at motor unit recruitment and de-recruitment. The ΔF technique provides insight into the magnitude of neuromodulatory synaptic input and persistent inward currents (PICs). While the ΔF technique is commonly used for estimating motoneuron excitability during voluntary contractions, computational parameters used for the technique vary across studies. A systematic investigation into the relationship between these parameters and ΔF values is necessary., Approach: We assessed the sensitivity of the ΔF technique with several criteria commonly used in selecting motor unit pairs for analysis and methods used for smoothing the instantaneous motor unit firing rates. Using high-density surface EMG and convolutive blind source separation, we obtained a large number of motor unit pairs (5409) from the triceps brachii of ten healthy individuals during triangular isometric contractions., Main Results: We found an exponential plateau relationship between ΔF and the recruitment time difference between the motor unit pairs and an exponential decay relationship between ΔF and the de-recruitment time difference between the motor unit pairs, with the plateaus occurring at approximately 1 s and 1.5 s, respectively. Reduction or removal of the minimum threshold for rate-rate correlation of the two units did not affect ΔF values or variance. Removing motor unit pairs in which the firing rate of the control unit was saturated had no significant effect on ΔF. Smoothing the filter selection had no substantial effect on ΔF values and ΔF variance; however, filter selection affected the minimum recruitment and de-recruitment time differences., Significance: Our results offer recommendations for standardized parameters for the ΔF approach and facilitate the interpretation of findings from studies that implement the ΔF analysis but use different computational parameters.

Analyses of motor unit activity provide a window to the neural control of motor output. In recent years, considerable advancements in surface EMG decomposition methods have allowed for the discrimination of dozens of individual motor units across a range of muscle forces. While these non-invasive methods show great potential as an emerging technology, they have difficulty discriminating a representative sample of the motor pool. In the present study, we investigate the distribution of recruitment thresholds and motor unit action potential waveforms obtained from high density EMG across four muscles: soleus, tibialis anterior, biceps brachii, and triceps brachii. Ten young and healthy control subjects generated isometric torque ramps between 10-50% maximum voluntary torque during elbow or ankle flexion and extension. Hundreds of motor unit spike trains were decomposed for each muscle across all trials. For lower contraction levels and speeds, surface EMG decomposition discriminated a large number of low-threshold units. However, during contractions of greater speed and torque level the proportion of low threshold motor units decomposed was reduced, resulting in a relatively uniform distribution of recruitment thresholds. The number of motor units decomposed decreased as the contraction level and speed increased. The decomposed units showed a wide range of recruitment thresholds and motor unit action potential amplitudes. In conclusion, although surface EMG decomposition is a useful tool to study large populations of motor units, results of such methods should be interpreted in the context of limitations in sampling of the motor pool.

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.

Surface electromyographic (EMG) signal amplitude is typically used to compare the neural drive to muscles. We experimentally investigated this association by studying the motor unit (MU) behavior and action potentials in the vastus medialis (VM) and vastus lateralis (VL) muscles. Eighteen participants performed isometric knee extensions at four target torques [10, 30, 50, and 70% of the maximum torque (MVC)] while high-density EMG signals were recorded from the VM and VL. The absolute EMG amplitude was greater for VM than VL ( P < 0.001), whereas the EMG amplitude normalized with respect to MVC was greater for VL than VM ( P < 0.04). Because differences in EMG amplitude can be due to both differences in the neural drive and in the size of the MU action potentials, we indirectly inferred the neural drives received by the two muscles by estimating the synaptic inputs received by the corresponding motor neuron pools. For this purpose, we analyzed the increase in discharge rate from recruitment to target torque for motor units matched by recruitment threshold in the two muscles. This analysis indicated that the two muscles received similar levels of neural drive. Nonetheless, the size of the MU action potentials was greater for VM than VL ( P < 0.001), and this difference explained most of the differences in EMG amplitude between the two muscles (~63% of explained variance). These results indicate that EMG amplitude, even following normalization, does not reflect the neural drive to synergistic muscles. Moreover, absolute EMG amplitude is mainly explained by the size of MU action potentials. NEW & NOTEWORTHY Electromyographic (EMG) amplitude is widely used to compare indirectly the strength of neural drive received by synergistic muscles. However, there are no studies validating this approach with motor unit data. Here, we compared between-muscles differences in surface EMG amplitude and motor unit behavior. The results clarify the limitations of surface EMG to interpret differences in neural drive between muscles.

The characteristics of motor unit force twitch profiles provide important information for the understanding of the muscle force generation. The twitch force is commonly estimated with the spike-triggered averaging technique, which, despite the many limitations, has been important for clarifying central issues in force generation. In this study, we propose a new technique for the estimation of the average twitch profile of populations of motor units with uniform contractile properties. The method encompasses a model-based deconvolution of the force signal using the identified discharge times of a population of motor units. The proposed technique was validated using simulations and tested on signals recorded during voluntary activation. The results of the simulations showed that the proposed method provides accurate estimates (relative error <25%) of the main parameters of the average twitch force when the number of identified motor units is between 5% and 15% of the total number of active motor units. It is discussed that current detection and decomposition methods of multi-channel surface EMG signals allow decoding this relative sample of the active motor unit pool. However, even when this condition is not met, our results show that the estimates provided by the new method are anyway always superior to those obtained by the spike triggered average approach, especially for high motor unit synchronization levels and when a relatively small number of triggers is available. In conclusion, we present a new method that overcome the main limitations of the spike-triggered average for the study of contractile properties of individual motor units. The method provides a new reliable tool for the investigation of the determinants of muscle force., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Wireless implanted devices can be used to interface patients with disabilities with the aim of restoring impaired motor functions. Implanted devices that record and transmit electromyographic (EMG) signals have been applied for the control of active prostheses. This simulation study investigates the propagation losses and the absorption rate of a wireless radio frequency link for in-to-on body communication in the medical implant communication service (MICS) frequency band to control myoelectric upper limb prostheses. The implanted antenna is selected and a suitable external antenna is designed. The characterization of both antennas is done by numerical simulations. A heterogeneous 3D body model and a 3D electromagnetic solver have been used to model the path loss and to characterize the specific absorption rate (SAR). The path loss parameters were extracted and the SAR was characterized, verifying the compliance with the guideline limits. The path loss model has been also used for a preliminary link budget analysis to determine the feasibility of such system compliant with the IEEE 802.15.6 standard. The resulting link margin of 11 dB confirms the feasibility of the system proposed., Competing Interests: The authors have declared that no competing interests exist.

Objective: The study of motor unit behavior has been classically performed by selective recording systems of muscle electrical activity (EMG signals) and decomposition algorithms able to discriminate between individual motor unit action potentials from multi-unit signals. In this study, we provide a general framework for the decomposition of multi-channel intramuscular and surface EMG signals and we extensively validate this approach with experimental recordings., Approach: First, we describe the conditions under which the assumptions of the convolutive blind separation model are satisfied. Second, we propose an approach of convolutive sphering of the observations followed by an iterative extraction of the sources. This approach is then validated using intramuscular signals recorded by novel multi-channel thin-film electrodes on the Abductor Digiti Minimi of the hand and Tibilias Anterior muscles, as well as on high-density surface EMG signals recorded by electrode grids on the First Dorsal Interosseous muscle. The validation was based on the comparison with the gold standard of manual decomposition (for intramuscular recordings) and on the two-source method (for comparison of intramuscular and surface EMG recordings) for the three human muscles and contraction forces of up to 90% MVC., Main Results: The average number of common sources identified for the validation was 14 ± 7 (averaged across all trials and subjects and all comparisons), with a rate of agreement in their discharge timings of 92.8 ± 3.2%. The average Decomposability Index, calculated on the automatic decomposed signals, was 16.0 ± 2.2 (7.3-44.1). For comparison, the same index calculated on the manual decomposed signals was 15.0 ± 3.0 (6.3-76.6)., Significance: These results show that the method provides a solid framework for the decomposition of multi-channel invasive and non-invasive EMG signals that allows the study of the behavior of a large number of concurrently active motor units.

Key Points: Reflex responses of single motor units have been used for the study of spinal circuitries but the methods employed are invasive and limited to the assessment of a relatively small number of motor units. We propose a new approach to investigate reflexes on individual motor units based on high-density surface electromyography (HDsEMG) decomposition. The decomposition of HDsEMG has been previously validated in voluntary isometric contractions but never during reflex activities. The use of HDsEMG decomposition for reflex studies at the individual motor unit level, during constant force contractions, with excitatory and inhibitory stimuli, was validated here by the comparison of results with concurrently recorded intramuscular EMG signals. The validation results showed that HDsEMG decomposition allows an accurate quantification of reflex responses for a large number of individual motor units non-invasively, for both excitatory and inhibitory stimuli., Abstract: We propose and validate a non-invasive method that enables accurate detection of the discharge times of a relatively large number of motor units during excitatory and inhibitory reflex stimulations. High-density surface electromyography (HDsEMG) and intramuscular EMG (iEMG) were recorded from the tibialis anterior muscle during ankle dorsiflexions performed at 5%, 10% and 20% of the maximum voluntary contraction (MVC) force, in nine healthy subjects. The tibial nerve (inhibitory reflex) and the peroneal nerve (excitatory reflex) were stimulated with constant current stimuli. In total, 416 motor units were identified from the automatic decomposition of the HDsEMG. The iEMG was decomposed using a state-of-the-art decomposition tool and provided 84 motor units (average of two recording sites). The reflex responses of the detected motor units were analysed using the peri-stimulus time histogram (PSTH) and the peri-stimulus frequencygram (PSF). The reflex responses of the common motor units identified concurrently from the HDsEMG and the iEMG signals showed an average disagreement (the difference between number of observed spikes in each bin relative to the mean) of 8.2 ± 2.2% (5% MVC), 6.8 ± 1.0% (10% MVC) and 7.5 ± 2.2% (20% MVC), for reflex inhibition, and 6.5 ± 4.1%, 12.0 ± 1.8% and 13.9 ± 2.4%, for reflex excitation. There was no significant difference between the characteristics of the reflex responses, such as latency, amplitude and duration, for the motor units identified by both techniques. Finally, reflex responses could be identified at higher force (4 of the 9 subjects performed contraction up to 50% MVC) using HDsEMG but not iEMG, because of the difficulty in decomposing the iEMG at high forces. In conclusion, single motor unit reflex responses can be estimated accurately and non-invasively in relatively large populations of motor units using HDsEMG. This non-invasive approach may enable a more thorough investigation of the synaptic input distribution on active motor units at various force levels., (© 2015 The Authors. The Journal of Physiology © 2015 The Physiological Society.)

Objective: The identification of common oscillatory inputs to motor neurons in the electromyographic (EMG) signal power spectrum is often preceded by EMG rectification for enhancing the low-frequency oscillatory components. However, rectification is a nonlinear operator and its influence on the EMG signal spectrum is not fully understood. In this study, we aim at determining when EMG rectification is beneficial in the study of oscillatory inputs to motor neurons., Approach: We provide a full mathematical description of the power spectrum of the rectified EMG signal and the influence of the average shape of the motor unit action potentials on it. We also provide a validation of these theoretical results with both simulated and experimental EMG signals., Main Results: Simulations using an advanced computational model and experimental results demonstrated the accuracy of the theoretical derivations on the effect of rectification on the EMG spectrum. These derivations proved that rectification is beneficial when assessing the strength of low-frequency (delta and alpha bands) common synaptic inputs to the motor neurons, when the duration of the action potentials is short, and when the level of cancellation is relatively low. On the other hand, rectification may distort the estimation of common synaptic inputs when studying higher frequencies (beta and gamma), in a way dependent on the duration of the action potentials, and may introduce peaks in the coherence function that do not correspond to physiological shared inputs., Significance: This study clarifies the conditions when rectifying the surface EMG is appropriate for studying neural connectivity.

Research on pattern recognition for myoelectric control has usually focused on a small number of electromyography (EMG) channels because of better clinical acceptability and low computational load with respect to multi-channel EMG. However, recently, high density (HD) EMG technology has substantially improved, also in practical usability, and can thus be applied in myocontrol. HD EMG provides several closely spaced recordings in multiple locations over the skin surface. This study considered the use of HD EMG for controlling upper limb prostheses, based on pattern recognition. In general, robustness and reliability of classical pattern recognition systems are influenced by electrode shift in dons and doff, and by the presence of malfunctioning channels. The aim of this study is to propose a new approach to attenuate these issues. The HD EMG grid of electrodes is an ensemble of sensors that records data spatially correlated. The experimental variogram, which is a measure of the degree of spatial correlation, was used as feature for classification, contrary to previous approaches that are based on temporal or frequency features. The classification based on the variogram was tested on seven able-bodied subjects and one subject with amputation, for the classification of nine and seven classes, respectively. The performance of the proposed approach was comparable with the classic methods based on time-domain and autoregressive features (average classification accuracy over all methods ∼ 95% for nine classes). However, the new spatial features demonstrated lower sensitivity to electrode shift ( ± 1 cm) with respect to the classic features . When even just one channel was noisy, the classification accuracy dropped by ∼ 10% for all methods. However, the new method could be applied without any retraining to a subset of high-quality channels whereas the classic methods require retraining when some channels are omitted. In conclusion, the new spatial feature space proposed in this study improved the robustness to electrode number and shift in myocontrol with respect to previous approaches.

Analysis of motor unit discharge can provide insight into the neural control of movement in healthy and pathological states, but it is typically completed in one muscle at a time. For some research investigations, it would be advantageous to study motor unit discharge from multiple muscles simultaneously. One such example is investigation of the flexion synergy, an abnormal muscle co-activation pattern in post-stroke individuals in which activation of shoulder abductors is involuntarily coupled with that of elbow and finger flexors. However, limitations in available technology have hindered the ability to efficiently extract motor unit discharge from multiple muscles simultaneously. In this study, we propose the use of high-density surface EMG decomposition from proximal and distal flexion synergy muscles (deltoid, biceps, wrist/finger flexors) in combination with an isometric joint torque recording device in individuals with chronic stroke. This innovative approach provides the ability to efficiently analyze both motor units and joint torques that have been simultaneously recorded from the shoulder, elbow, and fingers. In preliminary experiments, 3 stroke and 5 control participants generated shoulder abduction, elbow flexion, and finger flexion torques at 10, 20, 30 and 40% of maximum torque. Motor unit spike trains could be extracted from all muscles at each torque level. Mean motor unit firing rates were significantly lower in the stroke group than in the control group for all three muscles. Within the stroke group, wrist/finger flexor motor units had the lowest coefficient of variation. Additionally, modulation of mean firing rates across torque levels was significantly impaired in all three paretic muscles. The implications of these findings and overall impact of this approach are discussed.

The rectification of EMG signals is a preprocessing method widely used for inferring neural connectivity by coherence analysis. The assumption for the use of this non-linear operator is that it enhances the neural information in the signal, i.e. the motor unit spike trains. However, because of non-linearity, it is difficult to predict the effect of rectification on the EMG power spectrum. In this study, we analyze the influence of the motor unit action potential properties on spectral content of the rectified EMG. The results show that changes in the action potential waveforms have a strong influence on the rectified EMG power spectrum, with an effect on estimated coherence functions. Knowledge on the properties of action potentials may be necessary for properly comparing the rectified EMG power spectrum across conditions.

This study analyses the spatial distribution of individual motor unit potentials (MUPs) over the skin surface and the influence of motor unit depth and recording configuration on this distribution. Multichannel surface (13×5 electrode grid) and intramuscular (wire electrodes inserted with needles of lengths 15 and 25mm) electromyographic (EMG) signals were concurrently recorded with monopolar derivations from the biceps brachii muscle of 10 healthy subjects during 60-s isometric contractions at 20% of the maximum torque. Multichannel monopolar MUPs of the target motor unit were obtained by spike-triggered averaging of the surface EMG. Amplitude and frequency characteristics of monopolar and bipolar MUPs were calculated for locations along the fibers' direction (longitudinal), and along the direction perpendicular (transverse) to the fibers. In the longitudinal direction, monopolar and bipolar MUPs exhibited marked amplitude changes that extended for 16-32mm and 16-24mm over the innervation and tendon zones, respectively. The variation of monopolar and bipolar MUP characteristics was not symmetrical about the innervation zone. Motor unit depth had a considerable influence on the relative longitudinal variation of amplitude for monopolar MUPs, but not for bipolar MUPs. The transverse extension of bipolar MUPs ranged between 24 and 32mm, whereas that of monopolar MUPs ranged between 72 and 96mm. The mean power spectral frequency of surface MUPs was highly dependent on the transverse electrode location but not on depth. This study provides a basis for the interpretation of the contribution of individual motor units to the interference surface EMG signal., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Oscillatory common inputs of cortical or peripheral origin can be identified from the motor neuron output with coherence analysis. Linear transmission is possible despite the motor neuron non-linearity because the same input is sent commonly to several neurons. Because of the linear transmission, common input components to motor neurons can be investigated from the surface EMG, for example by EEG-EMG or EMG-EMG coherence. In these studies, there is an open debate on the utility and appropriateness of EMG rectification. The present study addresses this issue using an analytical, simulation and experimental approach. The main novel theoretical contribution that we report is that the spectra of both the rectified and the raw EMG contain input spectral components to motor neurons. However, they differ by the contribution of amplitude cancellation which influences the rectified EMG spectrum when extracting common oscillatory inputs. Therefore, the degree of amplitude cancellation has an impact on the effectiveness of EMG rectification in extracting input spectral peaks. The theoretical predictions were exactly confirmed by realistic simulations of a pool of motor neurons innervating a muscle in a cylindrical volume conductor of EMG generation and by experiments conducted on the first dorsal interosseous and the abductor pollicis brevis muscles of seven healthy subjects during pinching. It was concluded that when the contraction level is relatively low, EMG rectification may be preferable for identifying common inputs to motor neurons, especially when the energy of the action potentials in the low frequency range is low. Nonetheless, different levels of cancellation across conditions influence the relative estimates of the degree of linear transmission of oscillatory inputs to motor neurons when using the rectified EMG.

This review describes methods for interfacing motor neurons from muscle recordings and their applications in studies on the neural control of movement and in the design of technologies for neurorehabilitation. After describing methods for accessing the neural drive to muscles in vivo in humans, we discuss the mechanisms of transmission of synaptic input into motor neuron output and of force generation. The synaptic input received by a motor neuron population is largely common among motor neurons. This allows linear transmission of the input and a reduced dimensionality of control by the central nervous system. Force is generated by low-pass filtering the neural signal sent to the muscle. These concepts on neural control of movement are used for the development of neurorehabilitation technologies, which are discussed with representative examples on movement replacement, restoration, and neuromodulation. It is concluded that the analysis of the output of spinal motor neurons from muscle signals provides a unique means for understanding the neural coding of movement in vivo in humans and thus for reproducing this code artificially with the aim of restoring lost or impaired motor functions.

The aim of this study was to compare the decomposition results obtained from high-density surface electromyography (EMG) and concurrently recorded intramuscular EMG. Surface EMG signals were recorded with electrode grids from the tibialis anterior, biceps brachii, and abductor digiti minimi muscles of twelve healthy men during isometric contractions ranging between 5% and 20% of the maximal force. Bipolar intramuscular EMG signals were recorded with pairs of wire electrodes. Surface and intramuscular EMG were independently decomposed into motor unit spike trains. When averaged over all the contractions of the same contraction force, the percentage of discharge times of motor units identified by both decompositions varied in the ranges 84%-87% (tibialis anterior), 84%-86% (biceps brachii), and 87%-92% (abductor digiti minimi) across the force levels analyzed. This index of agreement between the two decompositions was linearly correlated with a self-consistency measure of motor unit discharge pattern that was based on coefficient of variation for the interspike interval (R(2) = 0.68 for tibialis anterior, R(2) = 0.56 for biceps brachii, and R(2) = 0.38 for abductor digiti minimi). These results constitute an important contribution to the validation of the noninvasive approach for the investigation of motor unit behavior in isometric low-force tasks.

Myoelectric control of active prostheses requires electrode systems that are easy to apply for daily repositioning of the electrodes by the user. In this study we propose the use of Smart Fabric and Interactive Textile (SFIT) systems as an alternative solution for recording high-density EMG signals for myoelectric control. A sleeve covering the upper and lower arm, which contains 100 electrodes arranged in four grids of 5 × 5 electrodes, was used to record EMG signals in 3 subjects during the execution of 9 tasks of the wrist and hand. The signals were analyzed by extracting wavelet coefficients which were classified with linear discriminant analysis. The average classification accuracy for the nine tasks was 89.1 ± 1.9 %. These results show that SFIT systems can be used as an effective way for muscle-machine interfacing.

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.

The study presents analytical, simulation, and experimental analyses of amplitude cancellation of motor-unit action potentials (APs) in the interference electromyogram (EMG) and its relation to the size of the spike-triggered average (STA) EMG. The amount of cancellation of motor-unit APs decreases monotonically as a function of the ratio between the root mean square (RMS) of the motor-unit AP and the RMS of the interference EMG signal. The theoretical derivation of this association indicates a method to measure cancellation in individual motor units by STA of the interference and squared EMGs. The theoretical relation was examined in both simulated EMG signals generated by populations of 200 motor units and experimental recordings of 492 and 184 motor-unit APs in the vastus medialis and abductor digiti minimi muscles, respectively. Although the theoretical relation predicted (R2 = 0.95; P < 0.001) the amount of cancellation in the simulated EMGs, the presence of motor-unit synchronization decreased the strength of the association for small APs. The decrease in size of the STA obtained from the squared EMG relative to that extracted from the interference EMG was predicted by the experimental measure of cancellation (R2 = 0.65; P < 0.001, for vastus medialis; R2 = 0.26; P < 0.05, for abductor digiti minimi). The results indicate that cancellation of APs in the interference EMG can be analytically predicted and experimentally measured with STA from the discharge times of the motor units into the surface EMG.

We propose a novel method for estimation of muscle fiber conduction velocity from surface electromyographic (EMG) signals. The method is based on the regression analysis between spatial and temporal frequencies of multiple dips introduced in the EMG power spectrum through the application of a set of spatial filters. This approach leads to a closed analytical expression of conduction velocity as a function of the auto- and cross-spectra of monopolar signals detected along the direction of muscle fibers. The performance of the algorithm was compared with respect to that of the classic single dip approach on simulated and experimental EMG signals. The standard deviation of conduction velocity estimates from simulated single motor unit action potentials was reduced from 1.51 m/s [10 dB signal-to-noise ratio (SNR)] and 1.06 m/s (20 dB SNR) with the single dip approach to 0.51 m/s (10 dB) and 0.23 m/s (20 dB) with the proposed method using 65 dips. When 200 active motor units were simulated in an interference EMG signal, standard deviation of conduction velocity decreased from 0.95 m/s (10 dB SNR) and 0.60 m/s (20 dB SNR) with a single dip to 0.21 m/s (10 dB) and 0.11 m/s (20 dB) with 65 dips. In experimental signals detected from the abductor pollicis brevis muscle, standard deviation of estimation decreased from (mean +/- SD over 5 subjects) 1.25 +/- 0.62 m/s with one dip to 0.10 +/- 0.03 m/s with 100 dips. The proposed method does not imply limitation in resolution of the estimated conduction velocity and does not require any iterative procedure for the estimate since it is based on a closed analytical formulation.

Alpha band oscillations in shared synaptic inputs to the alpha motor neuron pool can be considered an involuntary source of noise that hinders precise voluntary force production. This study investigated the impact of changing muscle length on the shared synaptic oscillations to spinal motor neurons, particularly in the physiological tremor band. Fourteen healthy individuals performed low‐level dorsiflexion contractions at ankle joint angles of 90° and 130°, while high‐density surface electromyography (HDsEMG) was recorded from the tibialis anterior (TA). We decomposed the HDsEMG into motor units spike trains and calculated the motor units' coherence within the delta (1–5 Hz), alpha (5–15 Hz), and beta (15–35 Hz) bands. Additionally, force steadiness and force spectral power within the tremor band were quantified. Results showed no significant differences in force steadiness between 90° and 130°. In contrast, alpha band oscillations in both synaptic inputs and force output decreased as the length of the TA was moved from shorter (90°) to longer (130°), with no changes in delta and beta bands. In a second set of experiments (10 participants), evoked twitches were recorded with the ankle joint at 90° and 130°, revealing longer twitch durations in the longer TA muscle length condition compared to the shorter. These experimental results, supported by a simple computational simulation, suggest that increasing muscle length enhances the muscle's low‐pass filtering properties, influencing the oscillations generated by the Ia afferent feedback loop. Therefore, this study provides valuable insights into the interplay between muscle biomechanics and neural oscillations. Key points: We investigated whether changes in muscle length, achieved by changing joint position, could influence common synaptic oscillations to spinal motor neurons, particularly in the tremor band (5–15 Hz).Our results demonstrate that changing muscle length from shorter to longer induces reductions in the magnitude of alpha band oscillations in common synaptic inputs. Importantly, these reductions were reflected in the oscillations of muscle force output within the alpha band.Longer twitch durations were observed in the longer muscle length condition compared to the shorter, suggesting that increasing muscle length enhances the muscle's low‐pass filtering properties.Changes in the peripheral contractile properties of motor units due to changes in muscle length significantly influence the transmission of shared synaptic inputs into muscle force output.These findings prove the interplay between muscle mechanics and neural adaptations. [ABSTRACT FROM AUTHOR]

The purpose of the study was to evaluate the influence of changes in ankle- and knee-joint angles on force steadiness and the discharge characteristics of motor units (MU) in soleus when the plantar flexors performed steady isometric contractions. Submaximal contractions (5, 10, 20, and 40% of maximum) were performed at two ankle angles (75° and 105°) and two knee angles (120° and 180°) by 14 young adults. The coefficient of variation of force decreased as the target force increased from 5 to 20% of maximal force, then remained unaltered at 40%. Independently of knee angle, the coefficient of variation for force at the ankle angle of 75° (long length) was always less (p<0.05) than that at 105° (shorter length). Mean discharge rate, discharge variability, and variability in neural activation of soleus motor units were less (p<0.05) at the 75° angle than at 105°. It was not possible to record MUs from medial gastrocnemius at the knee angle of 120° due to its minimal activation. The changes in knee-joint angle did not influence any of the outcome measures. The findings underscore the dominant role of the soleus muscle in the control of submaximal forces produced by the plantar flexor muscles. [ABSTRACT FROM AUTHOR]

This study determined the discharge characteristics of motor units from two lower limb muscles before and after fatiguing exercise in people with type 2 diabetes (T2D) with no symptoms of polyneuropathy and activity‐matched controls. Seventeen people with T2D (65.0 ± 5.6 years; 8 women) and 17 controls (63.6 ± 4.5 years; 8 women) performed: (a) intermittent, isometric contractions at 50% maximal voluntary isometric contraction (MVIC) sustained to failure with the ankle dorsiflexors, and (b) a dynamic fatiguing task (30% MVIC load) for 6 min with the knee extensors. Before and after the fatiguing tasks, motor unit characteristics (including coefficient of variation (CV) of interspike intervals (ISI)) were quantified from high‐density electromyography and muscle contractile properties were assessed via electrical stimulation. Fatigability was ~50% greater for people with T2D than controls for the dorsiflexors (time‐to‐failure: 7.3 ± 4.1 vs. 14.3 ± 9.1 min, p =.010) and knee extensors (power reduction: 56.7 ± 11.9 vs. 31.5 ± 25.5%, p <.001). The CV of ISI was greater for the T2D than control group for the tibialis anterior (23.1 ± 11.0 vs. 21.3 ± 10.7%, p <.001) and vastus lateralis (27.8 ± 20.2 vs. 24.5 ± 16.1%, p =.011), but these differences did not change after the fatiguing exercises. People with T2D had greater reductions in the electrically evoked twitch amplitude of the dorsiflexors (8.5 ± 5.1 vs. 4.0 ± 3.4%·min‐1, p =.013) and knee extensors (49.1 ± 10.0 vs. 31.8 ± 15.9%, p =.004) than controls. Although motor unit activity was more variable in people with T2D than controls, the greater fatigability of the T2D group for lower limb muscles was due to mechanisms involving disruption of contractile function of the exercising muscles rather than motor unit behavior. [ABSTRACT FROM AUTHOR]

We propose a myoelectric control method based on neural data regression and musculoskeletal modeling. This paradigm uses the timings of motor neuron discharges decoded by high-density surface electromyogram (HD-EMG) decomposition to estimate muscle excitations. The muscle excitations are then mapped into the kinematics of the wrist joint using forward dynamics. The offline tracking performance of the proposed method was superior to that of state-of-the-art myoelectric regression methods based on artificial neural networks in two amputees and in four out of six intact-bodied subjects. In addition to joint kinematics, the proposed data-driven model-based approach also estimated several biomechanical variables in a full feed-forward manner that could potentially be useful in supporting the rehabilitation and training process. These results indicate that using a full forward dynamics musculoskeletal model directly driven by motor neuron activity is a promising approach in rehabilitation and prosthetics to model the series of transformations from muscle excitation to resulting joint function. [ABSTRACT FROM AUTHOR]

Key points: Previous studies have indicated that several weeks of strength training is sufficient to elicit significant adaptations in the neural drive sent to the muscles.There are few data, however, on the changes elicited by strength training in the recruitment and rate coding of motor units during voluntary contractions. We show for the first time that the discharge characteristics of motor units in the tibialis anterior muscle tracked across the intervention are changed by 4 weeks of strength training with isometric voluntary contractions.The specific adaptations included significant increases in motor unit discharge rate, decreases in the recruitment‐threshold force of motor units and a similar input–output gain of the motor neurons.The findings suggest that the adaptations in motor unit function may be attributable to changes in synaptic input to the motor neuron pool or to adaptations in intrinsic motor neuron properties. The strength of a muscle typically begins to increase after only a few sessions of strength training. This increase is usually attributed to changes in the neural drive to muscle as a result of adaptations at the cortical or spinal level. We investigated the change in the discharge characteristics of large populations of longitudinally tracked motor units in tibialis anterior before and after 4 weeks of strength training the ankle‐dorsiflexor muscles with isometric contractions. The adaptations exhibited by 14 individuals were compared with 14 control subjects. High‐density electromyogram grids with 128 electrodes recorded the myoelectric activity during isometric ramp contractions to the target forces of 35%, 50% and 70% of maximal voluntary force. The motor unit recruitment and derecruitment thresholds, discharge rate, interspike intervals and estimates of synaptic inputs to motor neurons were assessed. The normalized recruitment‐threshold forces of the motor units were decreased after strength training (P < 0.05). Moreover, discharge rate increased by 3.3 ± 2.5 pps (average across subjects and motor units) during the plateau phase of the submaximal isometric contractions (P < 0.001). Discharge rates at recruitment and derecruitment were not modified by training (P < 0.05). The association between force and motor unit discharge rate during the ramp‐phase of the contractions was also not altered by training (P < 0.05). These results demonstrate for the first time that the increase in muscle force after 4 weeks of strength training is the result of an increase in motor neuron output from the spinal cord to the muscle. Key points: Previous studies have indicated that several weeks of strength training is sufficient to elicit significant adaptations in the neural drive sent to the muscles.There are few data, however, on the changes elicited by strength training in the recruitment and rate coding of motor units during voluntary contractions. We show for the first time that the discharge characteristics of motor units in the tibialis anterior muscle tracked across the intervention are changed by 4 weeks of strength training with isometric voluntary contractions.The specific adaptations included significant increases in motor unit discharge rate, decreases in the recruitment‐threshold force of motor units and a similar input–output gain of the motor neurons.The findings suggest that the adaptations in motor unit function may be attributable to changes in synaptic input to the motor neuron pool or to adaptations in intrinsic motor neuron properties. [ABSTRACT FROM AUTHOR]

Spike-triggered averaging is a commonly used technique for the estimation of motor unit twitches during voluntary contractions, although the obtained twitch estimates are known to be inaccurate in several conditions. Nevertheless, it is commonly assumed that a careful selection of the triggers may reduce the inaccuracy. This study aimed to analyze the impact of trigger selection criteria and thereby to identify the minimum estimation errors using a computational neuromuscular model. Force signals of five-minute duration were simulated at 10 contraction levels between 1 and 30% of the maximal voluntary contraction level (MVC) for motor unit pools of varying size (100, 300, and 800 motor units). Triggers were selected based on the inter-spike intervals (minimal value: 90-175 ms) and the number of triggers (minimal value: 100-800). The simulation results indicated that a minimum of 400 triggers with inter-spike intervals >125 ms are needed to achieve the most accurate twitch estimates. Even under these conditions, however, a substantial estimation error remained (11.8-31.2% for different twitch parameters for simulations with 100 motor units). The error increased with the innervation number. The study demonstrates the fundamental inaccuracy of twitch estimates from spike-triggered averaging, which has important implications for our understanding of muscular adaptations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Ischemic conditioning (IC) on the arm or leg has emerged as an intervention to improve strength and performance in healthy populations, but the effects on neurological populations are unknown. The purpose of this study was to quantify the effects of a single session of IC on knee extensor strength and muscle activation in chronic stroke survivors. Maximal knee extensor torque measurements and surface EMG were quantified in 10 chronic stroke survivors (>1 yr poststroke) with hemiparesis before and after a single session of IC or sham on the paretic leg. IC consisted of 5 min of compression with a proximal thigh cuff (inflation pressure = 225 mmHg for IC or 25 mmHg for sham) followed by 5 min of rest. This was repeated five times. Maximal knee extensor strength, EMG magnitude, and motor unit firing behavior were measured before and immediately after IC or sham. IC increased paretic leg strength by 10.6 ± 8.5 Nm, whereas no difference was observed in the sham group (change in sham = 1.3 ± 2.9 Nm, P = 0.001 IC vs. sham). IC-induced increases in strength were accompanied by a 31 ± 15% increase in the magnitude of muscle EMG during maximal contractions and a 5% decrease in motor unit recruitment thresholds during submaximal contractions. Individuals who had the most asymmetry in strength between their paretic and nonparetic legs had the largest increases in strength (r2 = 0.54). This study provides evidence that a single session of IC can increase strength through improved muscle activation in chronic stroke survivors. NEW & NOTEWORTHY Present rehabilitation strategies for chronic stroke survivors do not optimally activate paretic muscle, and this limits potential strength gains. Ischemic conditioning of a limb has emerged as an effective strategy to improve muscle performance in healthy individuals but has never been tested in neurological populations. In this study, we show that ischemic conditioning on the paretic leg of chronic stroke survivors can increase leg strength and muscle activation while reducing motor unit recruitment thresholds. [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]

α-Motor neurons receive synaptic inputs from spinal and supraspinal centers that comprise components either common to the motor neuron pool or independent. The input shared by motor neurons--common input--determines force control. The aim of the study was to investigate the changes in the strength of common synaptic input delivered to motor neurons with changes in force and with fatigue, two conditions that underlie an increase in the net excitatory drive to the motor neurons. High-density surface electromyogram (EMG) signals were recorded from the tibialis anterior muscle during contractions at 20, 50, and 75% of the maximal voluntary contraction force (in 3 sessions separated by at least 2 days), all sustained until task failure. EMG signal decomposition identified the activity of a total of 1,245 motor units. The coherence values between cumulative motor unit spike trains increased with increasing force, especially for low frequencies. This increase in coherence was not observed when comparing two subsets of motor units having different recruitment thresholds, but detected at the same force level. Moreover, the coherence values for frequencies <5 Hz increased at task failure with respect to the beginning of the contractions for all force levels. In conclusion, the results indicated that the relative strength of common synaptic input to motor neurons increases with respect to independent input when the net excitatory drive to motor neurons increases as a consequence of a change in force and fatigue. [ABSTRACT FROM AUTHOR]

Introduction: The aim of the study was to compare maximal force, force steadiness, and the discharge characteristics of motor units in the tibialis anterior (TA) muscle during submaximal isometric contractions for ankle dorsiflexion and adduction of the foot. Methods: Nineteen active young adults performed maximal and submaximal isometric dorsiflexion and adduction contractions at five target forces (5%, 10%, 20%, 40%, and 60% maximal voluntary contraction [MVC]). The activity of motor units in TA was recorded by high-density EMG. Results: The maximal force was similar between dorsiflexion and adduction, despite EMG amplitude for TA being greater (P < 0.05) during dorsiflexion than adduction. The coefficient of variation (CV) for force (force steadiness) during dorsiflexion was always less (P < 0.05) than during adduction, except of 5% MVC force. No differences were observed for mean discharge rate; however, the regression between the changes in discharge rate relative to the change of force was significant for dorsiflexion (R² = 0.25, P < 0.05) but not for adduction. Discharge variability, however, was usually less during dorsiflexion. The CV for interspike interval was less (P < 0.05) at 10%, 20%, and 40% MVC but greater at 60% MVC during dorsiflexion than adduction. Similarly, the SD values of the filtered cumulative spike train of the motor units in TA were less (P < 0.05) at 5%, 10%, 20%, and 40% MVC during dorsiflexion than adduction. Conclusions: Although the mean discharge rate of motor units in TA was similar during foot adduction and ankle dorsiflexion, discharge variability was less during dorsiflexion resulting in less accurate performance of the steady adduction contractions. The neural drive to bifunctional muscles differs during their accessory function, which must be considered for training and rehabilitation interventions. [ABSTRACT FROM AUTHOR]

Supplemental digital content is available in the text. Purpose: Moderate-intensity continuous training (MICT) and high-intensity interval training (HIIT) are associated with different adjustments in motor output. Changes in motor unit (MU) peripheral properties may contribute to these adjustments, but this is yet to be elucidated. This study evaluated early changes in MU conduction velocity (MUCV) and MU action potential amplitude after 2 wk of either HIIT or MICT. Methods: Sixteen men were assigned to either an MICT group or HIIT group (n = 8 each), and participated in six training sessions over 14 d. HIIT: 8 to 12 × 60-s intervals at 100% peak power output. Moderate-intensity continuous training: 90 to 120 min continuous cycling at ~65% V˙O2peak. Preintervention and postintervention, participants performed maximal voluntary contractions (MVC) and submaximal (10%, 30%, 50%, and 70% of MVC) isometric knee extensions while high-density EMG was recorded from the vastus medialis (VM) and vastus lateralis (VL) muscles. The high-density EMG was decomposed into individual MU by convolutive blind-source separation and tracked preintervention and postintervention. Results: Both training interventions induced changes in MUCV, but these changes depended on the type of training (P < 0.001). The HIIT group showed higher values of MUCV after training at all torque levels (P < 0.05), MICT only displayed changes in MUCV at low torque levels (10%–30% MVC, P < 0.002). There were no changes in MU action potential amplitude for either group (P = 0.2). Conclusions: Two weeks of HIIT or MICT elicit differential changes in MUCV, likely due to the contrasting load and volume used in such training regimes. This new knowledge on the neuromuscular adaptations to training has implications for exercise prescription. [ABSTRACT FROM AUTHOR]

Purpose: Using a novel technique of high-density surface EMG decomposition and motor unit (MU) tracking, we compared changes in the properties of vastus medialis and vastus lateralis MU after endurance (END) and high-intensity interval training (HIIT). Methods: Sixteen men were assigned to the END or the HIIT group (n = 8 each) and performed six training sessions for 14 d. Each session consisted of 8-12 x 60-s intervals at 100% peak power output separated by 75 s of recovery (HIIT) or 90-120 min continuous cycling at ~65% ...O2peak (END). Pre- and postintervention, participants performed 1) incremental cycling to determine ...O2peak and peak power output and 2) maximal, submaximal (10%, 30%, 50%, and 70% maximum voluntary contraction [MVC]), and sustained (until task failure at 30% MVC) isometric knee extensions while high-density surface EMG signals were recorded from the vastus medialis and vastus lateralis. EMG signals were decomposed (submaximal contractions) into individual MU by convolutive blind source separation. Finally, MU were tracked across sessions by semiblind source separation. Results: After training, END and HIIT improved ...O2peak similarly (by 5.0% and 6.7%, respectively). The HIIT group showed enhanced maximal knee extension torque by ~7% (P = 0.02) and was accompanied by an increase in discharge rate for high-threshold MU (≥50% knee extension MVC) (P < 0.05). By contrast, the END group increased their time to task failure by ~17% but showed no change in MU discharge rates (P > 0.05). Conclusions: HIIT and END induce different adjustments in MU discharge rate despite similar improvements in cardiopulmonary fitness. Moreover, the changes induced by HIIT are specific for high-threshold MU. For the first time, we show that HIIT and END induce specific neuromuscular adaptations, possibly related to differences in exercise load intensity and training volume. [ABSTRACT FROM AUTHOR]

Sex-related disparities in force production of humans have been widely observed. Previous literature has attributed differences in peripheral traits, such as muscle size, to explain these disparities. However, less is known about potential sex-related differences in central neuromuscular traits and many comparable studies, not exploring sex-related differences, exhibit a selection-bias in the recruitment of subjects making the generalization of their findings difficult. Utilizing high-density electromyography arrays and motor unit (MU) decomposition, the aim of the current study is to compare MU yield and discharge properties of the tibialis anterior between male and female humans. Twenty-four subjects (10 females) performed two submaximal (20%) isometric dorsiflexion contractions. On average, males yielded nearly twice the amount of MUs as females. Further, females had significantly higher MU discharge rate, lower MU action potential amplitude, and lower MU action potential frequency content than males despite similar levels of torque and MU discharge variability. These findings suggest differences in central neuromuscular control of force production between sexes; however, it is unclear how lower yield counts affect the accuracy of these results. [ABSTRACT FROM AUTHOR]

• There is an urgent need for clear guidelines, instructions and open-source software to analyse motor units' activity from High-Density Electromyography recordings. • This tutorial guides the reader through the post-decomposition analysis of MUs properties providing both theoretical knowledge and practical tools. • In this context, we introduce openhdemg , an innovative, free, and open-source framework for the automated analysis of motor units' properties. High-Density surface Electromyography (HD-sEMG) is the most established technique for the non-invasive analysis of single motor unit (MU) activity in humans. It provides the possibility to study the central properties (e.g., discharge rate) of large populations of MUs by analysis of their firing pattern. Additionally, by spike-triggered averaging, peripheral properties such as MUs conduction velocity can be estimated over adjacent regions of the muscles and single MUs can be tracked across different recording sessions. In this tutorial, we guide the reader through the investigation of MUs properties from decomposed HD-sEMG recordings by providing both the theoretical knowledge and practical tools necessary to perform the analyses. The practical application of this tutorial is based on openhdemg , a free and open-source community-based framework for the automated analysis of MUs properties built on Python 3 and composed of different modules for HD-sEMG data handling, visualisation, editing, and analysis. openhdemg is interfaceable with most of the available recording software, equipment or decomposition techniques, and all the built-in functions are easily adaptable to different experimental needs. The framework also includes a graphical user interface which enables users with limited coding skills to perform a robust and reliable analysis of MUs properties without coding. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine whether HD-sEMG is sensitive to detecting changes in motor unit behavior amongst healthy adults and type 2 diabetes mellitus (T2DM) patients presenting diabetic peripheral neuropathy (DPN) at different levels. Healthy control subjects (CON, n = 8) and T2DM patients presenting no DPN symptoms (ABS, n = 8), moderate DPN (MOD, n = 18), and severe DPN (SEV, n = 12) performed isometric ankle dorsiflexion at 30 % maximum voluntary contraction while high-density surface EMG (HD-sEMG) was recorded from the tibialis anterior muscle. HD-sEMG signals were decomposed, providing estimates of discharge rate, motor unit conduction velocity (MUCV), and motor unit territory area (MUTA). As a result, the ABS group presented reduced MUCV compared to CON. The groups with diabetes presented significantly larger MUTA compared to the CON group (p < 0.01), and the SEV group presented a significantly lower discharge rate compared to CON and ABS (p < 0.01). In addition, the SEV group presented significantly higher CoVforce compared to CON and MOD. These results support the use of HD-SEMG as a method to detect peripheral and central changes related to DPN. [ABSTRACT FROM AUTHOR]

Acute changes in central control and peripheral properties of motor units following a half-marathon has never been examined in master athletes. Therefore, the main purpose of this study was to estimate the firing properties and twitch characteristics of motor units after a 21-km race in a group of ten trained older adults. High-density surface EMG decomposition was used to identify motor unit activity during a submaximal contraction of the tibialis anterior muscle before and after the half marathon. The area of the estimated motor unit twitch profile was found smaller after the race (P = 0.039). This reduction in contractile efficiency was compensated by a significant increase in the initial and average discharge rate of the identified motor units (P < 0.001). By estimating the amount of shared and independent synaptic input to tibialis anterior motor neurons, we demonstrated that adaptations in the discharge properties of master athletes' motor units are the likely consequence of an increased net excitatory synaptic drive to the motor neuron pool. These findings suggest a potential role of long-distance running in ameliorating declines in muscle function of older adults by enhancing the neural drive to muscle. [ABSTRACT FROM AUTHOR]