Electromyographic Decomposition Research Articles

Adaptive EMG decomposition in dynamic conditions based on online learning metrics with tunable hyperparameters

Objective. Developing neural decoders robust to non-stationary conditions is essential to ensure their long-term accuracy and stability. This is particularly important when decoding the neural drive to muscles during dynamic contractions, which pose significant challenges for stationary decoders. Approach. We propose a novel adaptive electromyography (EMG) decomposition algorithm that builds on blind source separation methods by leveraging the Kullback–Leibler divergence and kurtosis of the signals as metrics for online learning. The proposed approach provides a theoretical framework to tune the adaptation hyperparameters and compensate for non-stationarities in the mixing matrix, such as due to dynamic contractions, and to identify the underlying motor neuron (MN) discharges. The adaptation is performed in real-time (∼22 ms of computational time per 100 ms batches). Main results. The hyperparameters of the proposed adaptation captured anatomical differences between recording locations (forearm vs wrist) and generalised across subjects. Once optimised, the proposed adaptation algorithm significantly improved all decomposition performance metrics with respect to the absence of adaptation in a wide range of motion of the wrist (80∘). The rate of agreement, sensitivity, and precision were ⩾90% in ⩾80% of the cases in both simulated and experimentally recorded data, according to a two-source validation approach. Significance. The findings demonstrate the suitability of the proposed online learning metrics and hyperparameter optimisation to compensate the induced modulation and accurately decode MN discharges in dynamic conditions. Moreover, the study proposes an experimental validation method for EMG decomposition in dynamic tasks.

Tutorial on MUedit: An open-source software for identifying and analysing the discharge timing of motor units from electromyographic signals

We introduce the open-source software MUedit and we describe its use for identifying the discharge timing of motor units from all types of electromyographic (EMG) signals recorded with multi-channel systems. MUedit performs EMG decomposition using a blind-source separation approach. Following this, users can display the estimated motor unit pulse trains and inspect the accuracy of the automatic detection of discharge times. When necessary, users can correct the automatic detection of discharge times and recalculate the motor unit pulse train with an updated separation vector. Here, we provide an open-source software and a tutorial that guides the user through (i) the parameters and steps of the decomposition algorithm, and (ii) the manual editing of motor unit pulse trains. Further, we provide simulated and experimental EMG signals recorded with grids of surface electrodes and intramuscular electrode arrays to benchmark the performance of MUedit. Finally, we discuss advantages and limitations of the blind-source separation approach for the study of motor unit behaviour during tonic muscle contractions.

2CFastICA: A Novel Method for High Density Surface EMG Decomposition Based on Kernel Constrained FastICA and Correlation Constrained FastICA.

This study presents a novel high density surface electromyography (EMG) decomposition method, named as 2CFastICA, because it incorporates two key algorithms: kernel constrained FastICA and correlation constrained FastICA. The former focuses on overcoming the local convergence of FastICA without requiring the peel-off strategy used in the progressive FastICA peel-off (PFP) framework. The latter further refines the output of kernel constrained FastICA by correcting possible erroneous or missed spikes. The two constrained FastICA algorithms supplement each other to warrant the decomposition performance. The 2CFastICA method was validated using simulated surface EMG signals with different motor unit numbers and signal to noise ratios (SNRs). Two source validation was also performed by simultaneous high density surface EMG and intramuscular EMG recordings, showing a matching rate (MR) of (97.2 ± 3.5)% for 170 common motor units. In addition, a different form of two source validation was also conducted taking advantages of the high density surface EMG characteristics of patients with amyotrophic lateral sclerosis, showing a MR of (99.4 ± 0.9)% for 34 common motor units from interference and sparse datasets. Both simulation and experimental results indicate that 2CFastICA can achieve similar decomposition performance to PFP. However, the efficiency of decomposition can be greatly improved by 2CFastICA since the complex signal processing procedures associated with the peel-off strategy are not required any more. Along with this paper, we also provide the MATLAB open source code of 2CFastICA for high density surface EMG decomposition.

Neural drive and motor unit characteristics after anterior cruciate ligament reconstruction: implications for quadriceps weakness.

The purpose of this investigation was to compare the quality of neural drive and recruited quadriceps motor units' (MU) action potential amplitude (MUAPAMP) and discharge rate (mean firing rate (MFR)) relative to recruitment threshold (RT) between individuals with anterior cruciate ligament reconstruction (ACLR) and controls. Fourteen individuals with ACLR and 13 matched controls performed trapezoidal knee extensor contractions at 30%, 50%, 70%, and 100% of their maximal voluntary isometric contraction (MVIC). Decomposition electromyography (dEMG) and torque were recorded concurrently. The Hoffmann reflex (H-reflex) and central activation ratio (CAR) were acquired bilaterally to detail the proportion of MU pool available and volitionally activated. We examined MUAPAMP-RT and MFR-RT relationships with linear regression and extracted the regression line slope, y-intercept, and RT range for each contraction. Linear mixed effect modelling used to analyze the effect of group and limb on regression line slope and RT range. Individuals with ACLR demonstrated lower MVIC torque in the involved limb compared to uninvolved limb. There were no differences in H-reflex or CAR between groups or limbs. The ACLR involved limb demonstrated smaller mass-normalized RT range and slower MU firing rates at high contraction intensities (70% and 100% MVIC) compared to uninvolved and control limbs. The ACLR involved limb also demonstrated larger MU action potentials in the VM compared to the contralateral limb. These differences were largely attenuated with relative RT normalization. These results suggest that persistent strength deficits following ACLR may be attributable to a diminished quadriceps motor neuron pool and inability to upregulate the firing rate of recruited MUs.

Larger and Denser: An Optimal Design for Surface Grids of EMG Electrodes to Identify Greater and More Representative Samples of Motor Units.

The spinal motor neurons are the only neural cells whose individual activity can be noninvasively identified. This is usually done using grids of surface electromyographic (EMG) electrodes and source separation algorithms; an approach called EMG decomposition. In this study, we combined computational and experimental analyses to assess how the design parameters of grids of electrodes influence the number and the properties of the identified motor units. We first computed the percentage of motor units that could be theoretically discriminated within a pool of 200 simulated motor units when decomposing EMG signals recorded with grids of various sizes and interelectrode distances (IEDs). Increasing the density, the number of electrodes, and the size of the grids, increased the number of motor units that our decomposition algorithm could theoretically discriminate, i.e., up to 83.5% of the simulated pool (range across conditions: 30.5-83.5%). We then identified motor units from experimental EMG signals recorded in six participants with grids of various sizes (range: 2-36 cm2) and IED (range: 4-16 mm). The configuration with the largest number of electrodes and the shortest IED maximized the number of identified motor units (56 ± 14; range: 39-79) and the percentage of early recruited motor units within these samples (29 ± 14%). Finally, the number of identified motor units further increased with a prototyped grid of 256 electrodes and an IED of 2 mm. Taken together, our results showed that larger and denser surface grids of electrodes allow to identify a more representative pool of motor units than currently reported in experimental studies.

Real-Time Hand Gesture Recognition by Decoding Motor Unit Discharges Across Multiple Motor Tasks From Surface Electromyography.

Surface electromyography (EMG) decomposition techniques have been developed to decode motor neuron activities non-invasively in the past decades, showing superior performance in human-machine interfaces such as gesture recognition and proportional control. However, neural decoding across multiple motor tasks and in real-time remains challenging, which limits its wide application. In this work, we proposed a real-time hand gesture recognition method by decoding motor unit (MU) discharges across multiple motor tasks ( 10) in a motion-wise way. The EMG signals were first divided into numerous segments related to motions. The convolution kernel compensation algorithm was applied for each segment individually. The local MU filters, which indicate the MU-EMG correlation for each motion, were calculated iteratively in each segment and reused for global EMG decomposition to trace the MU discharges across motor tasks in real-time. The motion-wise decomposition method was applied on the high-density EMG signals recorded during twelve hand gesture tasks from eleven non-disabled participants. The neural feature of discharge count was extracted for gesture recognition based on five common classifiers. On average, 164 ±34 MUs were identified for twelve motions from each subject, with a pulse-to-noise ratio of 32.1 ±5.6 dB. The average time cost of EMG decomposition in a sliding window of 50 ms was less than 5 ms. The average classification accuracy using a linear discriminant analysis classifier was 94.6 ±8.1%, which was significantly higher than that of a time-domain feature called root mean square. The superiority of the proposed method was also validated with a previously published EMG database comprising 65 gestures. These results indicate the feasibility and superiority of the proposed method for MU identification and hand gesture recognition across multiple motor tasks, extending the potential applications of neural decoding in human-machine interfaces.

A peel-off convolution kernel compensation method for surface electromyography decomposition

Objective:Estimating discharge patterns of motor units by electromyography (EMG) decomposition has shown promising perspectives in neurophysiologic investigations and human–machine interfaces. However, the number of decoded motor units is still limited, especially under high excitation and noisy conditions, which is a major barrier to the broad application of EMG decomposition. In this work, we applied a peel-off decomposition strategy to the convolution kernel compensation (CKC) algorithm to extract more motor units non-invasively. Methods:EMG signals were firstly decomposed into motor unit spike trains (MUSTs) using the CKC. After each decomposition, the motor unit action potentials (MUAPs) were reconstructed and subtracted from the EMG signals. Then the same CKC decomposition was applied to the residual signals. This peel-off procedure was repeated until no valid motor units were identified. The proposed decomposition strategy was validated on synthetic EMG signals by convolving simulated MUSTs and experimentally extracted MUAPs under multiple excitations and noise conditions. Then the decomposition performance was evaluated on experimental data. Main results:Compared with the classic CKC method, the number of the identified motor units from synthetic signals was significantly increased by 7 to 43 in each decomposition. Moreover, the average agreement between identified MUST and ground truth was 0.80 ± 0.20, indicating a high decomposition accuracy. From experimental EMG signals, the peel-off method could identify more motor units (>20) with high confidence than the classic method (<5) across three excitation levels. Conclusion and Significance:These results demonstrate the efficiency of the proposed method in identifying more motor units from EMG signals, extending the potential applications of surface EMG decomposition for neural decoding.

Simultaneous and Proportional Control of Wrist and Hand Movements Based on a Neural-Driven Musculoskeletal Model.

Human-machine interfaces (HMIs) based on electromyography (EMG) signals have been developed for simultaneous and proportional control (SPC) of multiple degrees of freedom (DoFs). The EMG-driven musculoskeletal model (MM) has been used in HMIs to predict human movements in prosthetic and robotic control. However, the neural information extracted from surface EMG signals may be distorted due to their limitations. With the development of high density (HD) EMG decomposition, accurate neural drive signals can be extracted from surface EMG signals. In this study, a neural-driven MM was proposed to predict metacarpophalangeal (MCP) joint flexion/extension and wrist joint flexion/extension. Ten non-disabled subjects (male) were recruited and tested. Four 64-channel electrode grids were attached to four forearm muscles of each subject to record the HD EMG signals. The joint angles were recorded synchronously. The acquired HD EMG signals were decomposed to extract the motor unit (MU) discharge for estimating the neural drive, which was then used as the input to the MM to calculate the muscle activation and predict the joint movements. The Pearson's correlation coefficient (r) and the normalized root mean square error (NRMSE) between the predicted joint angles and the measured joint angles were calculated to quantify the estimation performance. Compared to the EMG-driven MM, the neural-driven MM attained higher r values and lower NRMSE values. Although the results were limited to an offline application and to a limited number of DoFs, they indicated that the neural-driven MM outperforms the EMG-driven MM in prediction accuracy and robustness. The proposed neural-driven MM for HMI can obtain more accurate neural commands and may have great potential for medical rehabilitation and robot control.

Neural inputs from spinal motor neurons to lateralis vastus muscle: Comparison between sprinters and nonathletes.

The adaptation of neural contractile properties has been observed in previous work. However, the neural changes on the motor unit (MU) level remain largely unknown. Voluntary movements are controlled through the precise activation of MU populations. In this work, we estimate the neural inputs from the spinal motor neurons to the muscles during isometric contractions and characterize the neural adaptation during training by comparing the MU properties decomposed from sprinters and nonathletes. Twenty subjects were recruited and divided into two groups. The high-density surface electromyography (EMG) signals were recorded from the lateralis vastus muscle during the isometric contraction of knee extension and were then decomposed into MU spike trains. Each MU’s action potentials and discharge properties were extracted for comparison across subject groups and tasks. A total of 1097 MUs were identified from all subjects. Results showed that the discharge rates and amplitudes of MUAPs from athletes were significantly higher than those from nonathletes. These results demonstrate the neural adaptations in physical training at the MU population level and indicate the great potential of EMG decomposition in physiological investigations.

Differences in motor unit firing properties of the vastus lateralis muscle during postural and voluntary tasks.

The firing properties of the motor units are usually affected by the motor task. However, it has not been clarified whether the firing properties of the motor units of a specific muscle are different between postural and voluntary tasks. Therefore, this study investigated whether the recruitment and rate coding of the motor units differ between these two motor tasks. Thirteen healthy volunteers performed trapezoidal muscle contraction with a target value of 15% maximum electromyography (EMG) activity by voluntary left knee extension in the sitting position (voluntary task) and postural maintenance in the semi-squatting position (postural task) with a knee flexion angle of 30°. We obtained four channels of surface EMG activity during each task from left vastus lateralis muscle. We extracted the firing properties of individual motor units using the EMG decomposition algorithm. The recruitment threshold and motor unit action potential amplitude were significantly lower in the postural task than in the voluntary task, and conversely, the mean firing rate was significantly higher. These results were explained by the preferential recruitment of motor units with higher recruitment threshold and amplitude in the voluntary task, while motor units with lower recruitment threshold and higher firing rate were preferentially recruited in the postural task. Preferential activation of fatigue-resistant motor units in the postural task is a reasonable strategy as it allows for sustained postural maintenance. We provide the first evidence that motor unit firing properties are clearly different between postural and voluntary tasks, even at the same muscle activity level.

SCS 4th Annual Conference: Strength and Conditioning for Human Performance, Porto, Portugal, 2021.

On behalf of the Strength & Conditioning Society (SCS) and the Faculty of Sport of the University of Porto, we are pleased to present the abstracts of the SCS 4th Annual Conference: Strength and Conditioning for Human Performance, which took place in, Porto, Portugal, on 12–13 November 2021. The event was a success with invited sessions from renowned international and national speakers on a myriad of topics related to strength and conditioning and its application to health and sports performance, such as agility training and testing, high-intensity interval training in chronic conditions, hamstring strain injuries in soccer, and the utilization of surface electromyography (EMG) decomposition for assessing human performance, among others. During the Conference there were also different practical workshops on (1) velocity-based training; (2) performance testing and athlete monitoring using force platforms; (3) 3D kinematics tracking and flow force assessment in aquatic sports; (4) the application of inertial sensors for physical performance testing; (5) muscle fiber recruitment, force production, and energy expenditure in progressive bicycle testing; (6) EMG decomposition, motor-units recruitment, and muscle contraction modes; and (7) recovery strategies in team-sport athletes. Researchers and academics were able to present their latest findings by submitting the abstracts that compose this Conference Report.

Non-Invasive Analysis of Motor Unit Activation During Simultaneous and Continuous Wrist Movements.

Surface electromyography (EMG) signals have shown promising applications in human-machine interfacing (HMI) systems such as orthotics, prosthetics, and exoskeletons. Nevertheless, existing myoelectric control methods, generally based on time-domain or frequency-domain features, could not directly interpret neural commands. EMG decomposition techniques have become a prevailing solution to decode the motor neuron discharges from the spinal cord, whereas only single degree-of-freedom (DoF) movements are primarily involved in the current neural-based interfaces, resulting in limited intuitiveness and functionality. Here, we propose a non-invasive framework to analyze motor unit activities and estimate wrist torques during simultaneous contractions of multiple DoFs. Motor unit discharges were decoded from surface EMG signals and pooled into groups during sequential wrist movements. Then three neural features were extracted and linearly projected to the torques of multi-DoF tasks. On average, there were 44 ±13 motor units identified for each motion with a PNR value of 25.8 ±2.9 dB. The neural features outperformed the classic EMG feature on the estimation accuracy with higher correlation coefficients and smoothness. These results demonstrate the feasibility and superiority of the proposed framework in kinetics estimation of simultaneous movements, extending the potential applications of surface EMG decomposition in human-machine interfaces.

How to Work with Electromyography Decomposition in Practical Classes of Exercise Physiology and Biomechanics.

Concepts about motor unit recruitment are important learning contents in exercise physiology and biomechanics classes that are usually taught theoretically. In the last few years, great advances have occurred in the decomposition of surface electromyography, allowing the learning of theoretical contents in an experimental way. In this tutorial paper, we have described the decomposition of surface electromyography methodological aspects and examples to teach motor unit recruitment concepts in exercise physiology and biomechanics practical lessons. This work has the aim to facilitate physiology and biomechanics academics to introduce this technique in practical classes.

Analytical Modelling of Surface EMG Signals Generated by Curvilinear Fibers With Approximate Conductivity Tensor.

Mathematical modelling of surface electromyographic (EMG) signals has been proven a valuable tool to interpret experimental data and to validate signal processing techniques. Most analytical EMG models only consider muscle fibers with specific arrangements. However, the fiber orientation may change along the fiber paths and differ from fiber to fiber. Here we propose a subject-specific EMG model that simulates the fiber trajectories in muscles of the upper arm and analytically derives the action potentials assuming an approximate conductivity tensor. Magnetic Resonance (MR) images were acquired to generate muscle fiber paths and to build the volume conductor. While the action potentials propagated along the identified curvilinear fibers, the conductivity tensor was approximated to be cylindrically anisotropic. Single fiber action potentials (SFAPs) were computed by simulating the generation, propagation, and extinction of membrane current sources. To validate the assumption of the approximate conductivity tensor, two numerical models with approximate or exact conductivity tensors were implemented. The motor unit action potentials generated by the proposed analytical model and the two numerical models were highly similar (cross-correlation 0.98, normalized root mean square error, nRMSE ≤ 0.04, relative error in the median frequency of the simulated waveforms of approximately 3%). The proposed analytical model was also evaluated by comparing simulated and experimentally recorded compound muscle action potentials (CMAPs). Finally, the proposed model was used to test the accuracy of an EMG decomposition algorithm, providing a realistic benchmark. The proposed analytical model generates action potentials that reflect the spatial distributions of muscle fibers with curvilinear paths.

Segment-Wise Decomposition of Surface Electromyography to Identify Discharges Across Motor Neuron Populations.

The surface electromyography (EMG) decomposition techniques have shown promising results in neurophysiologic investigations, clinical diagnosis, and human-machine interfacing. However, current decomposition methods could only decode a limited number of motor units (MUs) because of the local convergence. The number of identified MUs remains similar even though more muscles or movements are involved, where multiple motor neuron populations are activated. The objective of this study was to develop a segment-wise decomposition strategy to increase the number of MU decoded from multiple motor neuron populations. The EMG signals were divided into several segments depending on the number of involved movements. The motor neurons, activated during each movement, were regarded as a population. The convolution kernel compensation (CKC) method was applied individually for each segment to decode the motor unit discharges from each motor neuron population. The MU filters were obtained in each segment and filtrated to estimate the MU spike trains (MUSTs) from the global EMG signals. The decomposition performance was validated on synthetic and experimental EMG signals. From synthetic EMG signals generated by two motor neuron populations, the proposed segment-wise CKC (swCKC) decoded significantly more MUs during low and medium excitation levels, with an increased rate of 16.3% to 75.4% compared with the conventional CKC. From experimental signals recorded during ten motor tasks, 133±24 MUs with the pulse-to-noise ratio of 36.6±6.5 dB were identified for each subject by swCKC, whereas the conventional CKC identified only 43±12 MUs. These results indicate the feasibility and superiority of the proposed swCKC to decode MU activities across motor neuron populations, extending the potential applications of EMG decomposition for neural decoding during multiple motor tasks.

Blockade of 5-HT2 receptors suppresses rate of torque development and motor unit discharge rate during rapid contractions.

Serotonin (5-HT) is a neuromodulator that is critical for regulating the excitability of spinal motoneurons and the generation of muscle torque. However, the role of 5-HT in modulating human motor unit activity during rapid contractions has yet to be assessed. Nine healthy participants (23.7 ± 2.2 yr) ingested 8 mg of the competitive 5-HT2 antagonist cyproheptadine in a double-blinded, placebo-controlled, repeated-measures experiment. Rapid dorsiflexion contractions were performed at 30%, 50%, and 70% of maximal voluntary contraction (MVC), where motor unit activity was assessed by high-density surface electromyographic decomposition. A second protocol was performed where a sustained, fatigue-inducing dorsiflexion contraction was completed before undertaking the same 30%, 50%, and 70% MVC rapid contractions and motor unit analysis. Motor unit discharge rate (P < 0.001) and rate of torque development (RTD; P = 0.019) for the unfatigued muscle were both significantly lower for the cyproheptadine condition. Following the fatigue inducing contraction, cyproheptadine reduced motor unit discharge rate (P < 0.001) and RTD (P = 0.024), whereas the effects of cyproheptadine on motor unit discharge rate and RTD increased with increasing contraction intensity. Overall, these results support the viewpoint that serotonergic effects in the central nervous system occur fast enough to regulate motor unit discharge rate during rapid powerful contractions.NEW & NOTEWORTHY We have shown that serotonin activity in the central nervous system plays a role in regulating human motor unit discharge rate during rapid contractions. Our findings support the viewpoint that serotonergic effects in the central nervous system are fast and are most prominent during contractions that are characterized by high motor unit discharge rates and large amounts of torque development.

Simultaneous and proportional control of wrist and hand movements by decoding motor unit discharges in real time

Objective. Surface electromyography (EMG) decomposition techniques can be used to establish human-machine interfacing (HMI), but most investigations are implemented offline due to the computational load of the approach. Here, we generalize the offline decomposition algorithm to identify the motor unit (MU) activities in real time, and we propose a MU-based approach for online simultaneous and proportional control (SPC) of multiple motor tasks. Approach. High-density surface EMG signals recorded from forearm muscles were decomposed into motor unit spike trains (MUSTs) with the proposed decomposition method. The MUSTs were first pooled into clusters in the calibration phase and the cumulative discharges of active MUs in each group were extracted as the control signal for each motor task. Then the subjects were instructed to control a virtual cursor with multiple motor tasks involving grasp and wrist movements. Fifteen able-bodied subjects and two patients with limb deficiency participated in the experiments to validate the proposed control scheme. Main results. On average, over 20 MUSTs were identified in real time with an estimated decomposition accuracy 85%. The cumulative discharge in each pool was highly correlated with the activation of the specific motion (R = 0.93 ± 0.05). Moreover, the proposed MU-based method had superior performance in online tests than conventional myo-control methods based on global EMG features. Significance. These results indicate the feasibility of real-time neural decoding in a non-invasive way. Moreover, the superior performance in online tests proves the potential of the MU-based approach for the SPC, promoting the application of EMG decomposition for HMI systems.

Analysis of motor unit activities during multiple motor tasks by real-time EMG decomposition: perspective for myoelectric control.

Surface electromyography (EMG) decomposition techniques have been applied for human-machine interfacing by decoding neural information, while most of decomposition approaches work offline. Here, we apply an online decomposition scheme to decode motor unit activities during three motor tasks, and measure the recognition accuracy of motor type and activation level using the decomposition results. High-density surface EMG signal were recorded from forearm muscles of six able-bodied subjects. The EMG signals were decomposed into motor unit spike trains (MUST) with a sliding window of 100 ms. The computation complexity had time consumption < 50 ms in each window. Most identified motor units discharged during only one motor task. On average, over 5 MUSTs were identified for each motion and the recognition accuracy based on motor unit activities was > 99%. The discharge rate of motor units was highly correlated with the activation level of each motion with an average correlation coefficient of 0.94 ± 0.04. These results indicate the feasibility of an online, multi-motion, and proportional control scheme based on neural decoding in a non-invasive way.

Measuring the accuracies of motor unit firing times and action potential waveforms derived from surface electromyographic decomposition

This study included spike trigger averaging (STA) procedures to examine the acceptability of the Precision Decomposition (PD) III derived motor unit action potential (MUAP) trains that met the >90% accuracy criteria from the reconstruct-and-test. MUs met the >90% accuracy criteria from the reconstruct-and-test with STA procedures then applied. Y-intercepts and slopes were calculated for the firing rate- and MUAP amplitude-recruitment threshold relationships. Gaussian noise (1% of the SD of the mean interspike interval) was added to the firing times with the changes in MUAPs quantified. A total of 455 MUs were decomposed with 155 MUs removed as a result of the reconstruct-and-test. Five additional MUs were excluded via the STA criteria. The MUAP waveforms deteriorated with the inclusion of Gaussian noise. There were differences in the derived action potentials amplitudes of higher-threshold MUs between the PD III algorithm and the STA procedure. There was excellent agreement among the slopes and y-intercepts between the relationships that included or excluded MUs that did not meet the STA criteria. There was good agreement between the MUAP amplitude-recruitment threshold relationships derived from the PD III and STA procedure. The addition of the STA procedures did not alter the MU-derived relationships.

Adaptive Real-Time Identification of Motor Unit Discharges From Non-Stationary High-Density Surface Electromyographic Signals.

Estimation of the discharge pattern of motor units by electromyography (EMG) decomposition has been applied for neurophysiologic investigations, clinical diagnosis, and human-machine interfacing. However, most of the methods for EMG decomposition are currently applied offline. Here, we propose an approach for high-density surface EMG decomposition in real-time. A real-time decomposition scheme including two sessions, offline training and online decomposition, is proposed based on the convolutional kernel compensation algorithm. The estimation parameters, separation vectors and the thresholds for spike extraction, are first computed during offline training, and then they are directly applied to estimate motor unit spike trains (MUSTs) during the online decomposition. The estimation parameters are updated with the identification of new discharges to adapt to non-stationary conditions. The decomposition accuracy was validated on simulated EMG signals by convolving synthetic MUSTs with motor unit action potentials (MUAPs). Moreover, the accuracy of the online decomposition was assessed from experimental signals recorded from forearm muscles using a signal-based performance metrics (pulse-to-noise ratio, PNR). The proposed algorithm yielded a high decomposition accuracy and robustness to non-stationary conditions. The accuracy of MUSTs identified from simulated EMG signals was 80% for most conditions. From experimental EMG signals, on average, 12 ± 2 MUSTs were identified from each electrode grid with PNR of 25.0 ± 1.8dB, corresponding to an estimated decomposition accuracy 75%. These results indicate the feasibility of real-time identification of motor unit activities non-invasively during variable force contractions, extending the potential applications of high-density EMG as a neural interface.