Motor Unit Action Potentials Research Articles

Post-procedural Brachial Neuritis: Clinical, Electrodiagnostic and Neuroimaging features.

Brachial neuritis is a monophasic condition affecting the brachial plexus and its branches, manifesting as acute shoulder and upper arm pain, followed by weakness and paresthesias. It can be triggered by antecedent events, including procedures such as surgery. Misdiagnosis and delay in diagnosis is common. Imaging is important to confirm the diagnosis of post-procedural brachial neuritis and exclude other etiologies. Clinical, electrodiagnostic, and neuroimaging features of patients with post-procedural brachial neuritis from a single quaternary care institution were identified and analyzed. Six (2 female) patients were identified with median age 62 (range 49-70) years. Antecedent procedures included 4 cervical spine surgeries, 1 rotator cuff repair, and 1 central venous catheter placement. Time to symptom onset ranged from 1-day to 2-weeks. The initial symptom for 5/6 patients was severe upper extremity pain followed by weakness. All patients had electrodiagnostic tests and MR neurography consistent with brachial neuritis. MR neurogram showed plexus and/or terminal branch abnormalities with associated muscular denervation edema. The C5 or C6 root, and/or upper trunk were always involved. The most common branches affected were the suprascapular, long thoracic, and axillary nerves. Hourglass constrictions of these nerves were seen in 3/6 patients. The average time to diagnosis was 3.4 (range 1.5-5) months. Post-procedural brachial neuritis is an underrecognized cause of acute upper extremity pain and weakness. MR neurography can exclude iatrogenic causes and document the presence of hourglass constrictions in affected nerves. Diagnostic neuroradiologists should be aware of this clinical entity and associated neuroimaging findings. HGC = hourglass constriction; EDX = electrodiagnostic; IRB = Institutional Review Board; ACDF = anterior cervical discectomy and fusion; NRS = Numerical Rating Scale; MUAP = motor unit action potential.

A comparison of electromyography techniques: surface versus intramuscular recording.

This review is a comprehensive guide for electromyography (EMG) researchers, providing a comparison of skin EMG recording (surface EMG: sEMG and high-density sEMG: HD-sEMG) and intramuscular EMG recording (multi-motor unit-MMU and single motor unit electromyography-SMU). We delve into the nuances of techniques, highlighting their strengths and limitations in quantifying muscle activation during dynamic and static conditions. We first examine how EMG signals change with time, focussing on the interplay between motor unit synchronisation and signal amplitude. The review then explores the impact of electrode placement on signal quality. We further discuss the challenges of signal cancellation, crosstalk from neighbouring muscles, and motion artifacts, which can significantly affect signal integrity. Finally, we address the temporal changes in electrode impedance and its implications for data interpretation. Our analysis proposes that specific research objectives should guide the choice amongst sEMG, HD-sEMG, SMU and MMU. MMU, which records the peak counts of individual motor unit action potentials from a localised muscle area, is particularly suited for studying deep or small muscles during static and dynamic activities. Its high sensitivity to motor unit recruitment and discharge rates minimises the impact of factors such as signal cancellation and motion artefacts. Conversely, sEMG is well-suited for short-duration, isometric assessments of large, superficial muscles. HD-sEMG helps study single motor unit properties under isometric conditions. SMU is particularly suited for studying neuronal networks between stimulated sites and motor neurons. This review aims toprovideresearchers with the information to select the most appropriate EMG technique for their investigations.

Unaltered Responses of Distal Motor Neurons to Non-Targeted Thoracic Spinal Cord Stimulation in Chronic Pain Patients.

Spinal cord stimulation (SCS) represents an established interventional pain therapeutic; however, the SCS effects of SCS waveforms on motor neuron recruitment of the lower limbs of chronic pain patients remain largely unknown. We investigated these effects by performing isometric ankle-dorsal flexions at varying force levels under four SCS conditions: SCS Off (1week), burst SCS (40Hz), SCS Off (acute), and tonic SCS (130Hz). Muscle activity was recorded via high-density surface electromyography (64-electrode grid) on the tibialis anterior muscle. Motor unit action (MUs) potentials were analyzed for recruitment and de-recruitment thresholds, discharge rate, inter-spike interval, and common synaptic input. In this prospective study, we included nine patients (five females; four males; mean age 59years) with chronic pain treated with thoracic (Th7-Th8) epidural spinal stimulation. A total of 97 MUs were found for 15% maximal voluntary torque (MVT) and 83 for 30%MVT, an average of 10.8±3.7 for 15%MVT and 10.4±3.5 for 30%MVT. While a few subject-specific variations were observed, our study suggests that the different SCS frequencies applied do not significantly influence motor unit discharge characteristics in the TA muscle among the participants (p values at 15%MVT were 0.586 (Chi2=1.933), 0.737 (Chi2=1.267), 0.706 (Chi2=1.4) and 0.586 (Chi2=1.933), respectively. The p values of the Friedman test at 30%MVT were 0.896 (Chi2=0.6), 0.583 (Chi2=1.95), 0.896 (Chi2=0.6) and 0.256 (Chi2=4.05). No significant difference was found for the different stimulation types for the delta (0-5Hz), alpha (5-12Hz), and beta (15-30Hz) bands at both force levels. In summary, we did not observe any changes in motor unit oscillatory activity at any low and high bandwidths, indicating that SCS using different waveforms (tonic/burst) does not significantly influence motor neuron recruitment for non-motor individuals with chronic pain.

Motor unit firing rates during slow and fast contractions in boys and men.

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

An efficient approach for EMG controlled pattern recognition system based on MUAP identification and segregation

An Electromyography (EMG) based pattern recognition system constitutes various steps of signal processing and control engineering from signal acquisition to real-time control. Efficient control of external devices largely depends on the signal processing steps executed before the final output. This work presents a new approach to signal processing using Motor Unit Action Potential (MUAP) based signal decomposition and segmentation. An MUAP is a neurological response during muscle contraction. Due to the higher contact area of surface electrodes, MUAPs from multiple muscles are captured. An MUAP generated from a single muscle usually has identical waveshapes and similar discharging rates and usually lasts for 8–15 ms. These are known as primary MUAPs. The proposed algorithm identifies and uses the primary observed MUAPs for feature extraction and classification. Firstly, noise signals are eliminated by a determined noise margin, which also separates the active muscle movement signals. Next, a novel MUAP identification algorithm is implemented to detect the MUAP trains. Then, identified primary MUAPs are used to make segments with variable widths to extract feature vectors. Based on the correlation score of all the primary MUAPs, the segmentation is performed, which results in segmentation width varying from 110–200 ms. The achieved segmentation width is lesser than the conventional overlapping and non-overlapping methods — the proposed approach results in a 20 to 50% reduction in the segmentation width. Four different classifiers are tested during the machine learning stage to investigate the performance of the proposed approach. The obtained feature sets are then used to train the Linear Discriminant Analysis (LDA), K-Nearest Neighbor (kNN), Decision Tree (DT), and Random Forest (RF) classifiers. The classifiers are tested with precision, recall, F1 score, and accuracy. The kNN and DT classifiers performed better than the LDA and RF classifiers. The maximum precision and recall are 100% while the maximum achieved accuracy is 98.56%. The comparative results show higher accuracy even at lower segmentation widths than the conventional constant window scheme. The kNN and DT classifiers provide a 5% to 15% increment in accuracy compared to the constant window segmentation-based approach.

LRRK2G2019S Gene Mutation Causes Skeletal Muscle Impairment in Animal Model of Parkinson's Disease

ABSTRACTBackgroundWhile the gradually aggravated motor and non‐motor disorders of Parkinson's disease (PD) lead to progressive disability and frequent falling, skeletal muscle impairment may contribute to this condition. The leucine‐rich repeat kinase2 (LRRK2) is a common disease‐causing gene in PD. Little is known about its role in skeletal muscle impairment and its underlying mechanisms.MethodsTo investigate whether the mutation in LRRK2 causes skeletal muscle impairment, we used 3‐month‐old (3mo) and 14‐month‐old (14mo) LRRK2G2019S transgenic (TG) mice as a model of PD, compared with the age‐matched littermate wild‐type (WT) controls. We measured the muscle mass and strength, ultrastructure, inflammatory infiltration, mitochondrial morphology and dynamics dysfunction through behavioural analysis, electromyography (EMG), immunostaining, transmission electron microscopy (TEM) and other molecular biology techniques.ResultsThe 3mo‐TG mice display mild skeletal muscle impairment with spontaneous potentials in EMG (increased by 130%, p &lt; 0.05), myofibre necrosis (p &lt; 0.05) and myosin heavy chain‐II changes (reduced by 19%, p &lt; 0.01). The inflammatory cells and macrophage infiltration are significantly increased (CD8a+ and CD68+ cells up 1060% and 579%, respectively, both p &lt; 0.0001) compared with the WT mice. All of the above pathogenic processes are aggravated by aging. The 14mo‐TG mice EMG examinations show a reduced duration (by 31%, p &lt; 0.01) and increased polyphasic waves of motor unit action potentials (by 28%, p &lt; 0.05). The 14mo‐TG mice present motor behavioural deficits (p &lt; 0.05), muscle strength and mass reduction by 37% and 8% (p &lt; 0.05 and p &lt; 0.01, respectively). A remarkable increase in inflammatory infiltration is accompanied by pro‐inflammatory cytokines in the skeletal muscles. TEM analysis shows muscle fibre regeneration with the reduced length of sarcomeres (by 6%;p &lt; 0.05). The muscle regeneration is activated as Pax7+ cells increased by 106% (p &lt; 0.0001), andmyoblast determination protein elevated by 71% (p &lt; 0.01). We also document the morphological changes and dynamics dysfunction of mitochondria with the increase of mitofusin1 by 43% (p &lt; 0.05) and voltage‐dependent anion channel 1 by 115% (p &lt; 0.001) in the skeletal muscles of 14mo‐TG mice.ConclusionsTaken together, these findings may provide new insights into the clinical and pathogenic involvement of LRRK2G2019 mutation in muscles, suggesting that the diseases may affect not only midbrain dopaminergic neurons, but also other tissues, and it may help overall clinical management of this devastating disease.

Insights into the Medial Pectoral Nerve Transfer for Shoulder Abduction in Brachial Plexus Injuries: A Retrospective Case Series Analysis

Treatment priority in C5, C6, and C7 brachial plexus root avulsion is the recovery of shoulder function through reinnervation of shoulder muscles. The medial pectoral nerve is a potential donor for axillary nerve transfer, but outcomes are sparsely reported. This study reports the results of medial pectoral nerve transfer to the axillary nerve. We conducted a retrospective analysis of 12 patients with traumatic brachial plexus injury (C5, C6, and C7 root avulsion) who underwent medial pectoral nerve transfer to the axillary nerve. Sociodemographic and clinical characteristics, including electromyography findings, were documented. We assessed postoperative shoulder abduction strength and range of motion. Statistical analyses compared presurgery and postsurgery outcomes and contrasted our results with those from a study using spinal accessory nerve transfer to the suprascapular nerve. Postsurgery, the mean shoulder abduction range of motion was 65.45°, with a median strength of M2. Significant improvement was noted compared to preoperative values. However, outcomes did not significantly surpass those from spinal accessory nerve transfer. Electromyography showed a low incidence of motor unit action potentials in the deltoid. Medial pectoral nerve transfer to the axillary nerve did not yield superior results in shoulder abduction and deltoid reinnervation in our group of patients. At present, different nerve donors may also need to be considered for deltoid muscle reinnervation in patients with C5, C6, and C7 root avulsion to achieve better shoulder abduction recovery.

Impact of Urethral Sphincter Electrophysiology on Botulinum Toxin A Treatment in Women with Non-Neurogenic Dysfunctional Voiding.

Dysfunctional voiding (DV) is an abnormal urethral sphincter activity during voiding in neurologically normal individuals. Urethral sphincter botulinum toxin A (BoNT-A) injection has been used to treat DV, but the results have not been completely satisfactory. This study investigated the neurological characteristics of women with DV using the lower urinary tract electrophysiology (EP) study and the therapeutic efficacy of BoNT-A injection. In total, 48 women with DV and 16 women with normal voiding were included. Videourodynamic studies were conducted to diagnose DV before BoNT-A injection. EP studies, including urethral sphincter electromyography, bulbocavernosus reflex, and pudendal nerve conduction velocity, were conducted. Polyphasic motor unit action potentials suggestive of reinnervation were detected in 58.3% of patients with DV and 18.8% of controls (p = 0.001). Significant improvement in the corrected maximum flow rate (cQmax) was observed in patients with reinnervation at 1 and 3 months after BoNT-A injections into the urethral sphincter. Urethral sphincter denervation or reinnervation activity was commonly noted in 62.5% of women with DV. Repeated BoNT-A injections into the urethral sphincter provided effective treatment in 47.9% of patients, with mild improvement in cQmax observed in patients with urethral sphincter reinnervation. However, the improvement was not superior to those without reinnervation.

NeuroMotion: Open-source platform with neuromechanical and deep network modules to generate surface EMG signals during voluntary movement.

Neuromechanical studies investigate how the nervous system interacts with the musculoskeletal (MSK) system to generate volitional movements. Such studies have been supported by simulation models that provide insights into variables that cannot be measured experimentally and allow a large number of conditions to be tested before the experimental analysis. However, current simulation models of electromyography (EMG), a core physiological signal in neuromechanical analyses, remain either limited in accuracy and conditions or are computationally heavy to apply. Here, we provide a computational platform to enable future work to overcome these limitations by presenting NeuroMotion, an open-source simulator that can modularly test a variety of approaches to the full-spectrum synthesis of EMG signals during voluntary movements. We demonstrate NeuroMotion using three sample modules. The first module is an upper-limb MSK model with OpenSim API to estimate the muscle fibre lengths and muscle activations during movements. The second module is BioMime, a deep neural network-based EMG generator that receives nonstationary physiological parameter inputs, like the afore-estimated muscle fibre lengths, and efficiently outputs motor unit action potentials (MUAPs). The third module is a motor unit pool model that transforms the muscle activations into discharge timings of motor units. The discharge timings are convolved with the output of BioMime to simulate EMG signals during the movement. We first show how MUAP waveforms change during different levels of physiological parameter variations and different movements. We then show that the synthetic EMG signals during two-degree-of-freedom hand and wrist movements can be used to augment experimental data for regressing joint angles. Ridge regressors trained on the synthetic dataset were directly used to predict joint angles from experimental data. In this way, NeuroMotion was able to generate full-spectrum EMG for the first use-case of human forearm electrophysiology during voluntary hand, wrist, and forearm movements. All intermediate variables are available, which allows the user to study cause-effect relationships in the complex neuromechanical system, fast iterate algorithms before collecting experimental data, and validate algorithms that estimate non-measurable parameters in experiments. We expect this modular platform will enable validation of generative EMG models, complement experimental approaches and empower neuromechanical research.

Decomposition strategy for surface EMG with few channels: a simulation study

Objective. In the specific use of electromyogram (EMG) driven prosthetics, the user’s disability reduces the space available for the electrode array. We propose a framework for EMG decomposition adapted to the condition of a few channels (less than 30 observations), which can elevate the potential of prosthetics in terms of cost and applicability. Approach. The new framework contains a peel-off approach, a refining strategy for motor unit (MU) spike train and MU action potential and a re-subtracting strategy to adapt the framework to few channels environments. Simulated EMG signals were generated to test the framework. In addition, we quantify and analyze the effect of strategies used in the framework. Main results. The results show that the new algorithm has an average improvement of 19.97% in the number of MUs identified compared to the control algorithm. Quantitative analysis of the usage strategies shows that the re-subtracting and refining strategies can effectively improve the performance of the framework under the condition of few channels. Significance. These prove that the new framework can be applied to few channel conditions, providing a optimization space for neural interface design in cost and user adaptation.

Spectral characterization of human leg EMG signals from an open access dataset for the development of computational models.

Large-scale neuromusculoskeletal models have been used for predicting mechanisms underlying neuromuscular functions in humans. Simulations of such models provide several types of signals of practical interest, such as surface electromyographic signals (EMG), which are compared with experimental data for interpretations of neurophysiological phenomena under study. Specifically, realistic characterization of spectral properties of simulated EMG signals is important for achieving powerful inferences, whereas considerations should be taken for myoelectric signals of different muscles. In this study, we characterized spectral properties of surface interference pattern EMG signals and motor unit action potentials (MUAP) acquired from three plantar flexor muscles: Soleus (SO), Medial Gastrocnemius (MG), and Lateral Gastrocnemius (LG); and one dorsiflexor muscle: Tibialis Anterior (TA). Surface EMG signals were acquired from 20 participants using the same convention for electrode placement. Specifically, interference pattern EMG signals were obtained during isometric constant force contractions at 5%, 10% and 20% of maximum voluntary contraction (MVC), whereas surface MUAPs were decomposed from surface EMG signals obtained at low contraction forces. We compared the spectrum median frequency (MDF) estimated from interference pattern EMG signals across muscles and contraction intensities. Additionally, we compared MDF and durations of MUAPs between muscles. Our results showed that MDF of interference pattern EMG signals acquired from TA were higher compared to SO, MG, and LG for all contraction intensities i.e., 5%, 10%, and 20% MVC. Consistently, MUAPs acquired from TA also had higher MDF values and shorter durations compared to the other leg muscles. We provide herein a dataset with the surface MUAPs waveforms and interference pattern EMG signals obtained for this study, which should be useful for implementing and validating the simulation of myoelectrical signals of leg muscles. Importantly, these results indicate that spectral properties of myoelectrical signals should be considered for improving EMG modeling in large-scale neuromusculoskeletal models.

Effect of Warm-Up Exercise on Functional Regulation of Motor Unit Activation during Isometric Torque Production.

In this study, we tested several hypotheses related to changes in motor unit activation patterns after warm-up exercise. Fifteen healthy young men participated in the experiment and the main task was to produce voluntary torque through the elbow joint under the isometric condition. The experimental conditions consisted of two directions of torque, including flexion and extension, at two joint angles, 10° and 90°. Participants were asked to increase the joint torque to the maximal level at a rate of 10% of the maximum voluntary torque. The warm-up protocol followed the ACSM guidelines, which increased body temperature by approximately 1.5°C. Decomposition electromyography electrodes, capable of extracting multiple motor unit action potentials from surface signals, were placed on the biceps and triceps brachii muscles, and joint torque was measured on the dynamometer. The mean firing rate and the recruitment threshold of the decomposed motor units were quantified. In addition, a single motor unit activity from the spike train was quantified for each of five selected motor units. The magnitude of joint torque increased with the warm-up exercise for all the experimental conditions. The results of the motor unit analyses showed a positive and beneficial effect of the warm-up exercise, with an increase in both the mean firing rate and the recruitment threshold by about 56% and 33%, respectively, particularly in the agonist muscle. Power spectral density in the gamma band, which is thought to be the dominant voluntary activity, was also increased by the warm-up exercise only in the high threshold motor units.

Inferring position of motor units from high-density surface EMG

The spatial distribution of muscle fibre activity is of interest in guiding therapy and assessing recovery of motor function following injuries of the peripheral or central nervous system. This paper presents a new method for stable estimation of motor unit territory centres from high-density surface electromyography (HDsEMG). This completely automatic process applies principal component compression and a rotatable Gaussian surface fit to motor unit action potential (MUAP) distributions to map the spatial distribution of motor unit activity. Each estimated position corresponds to the signal centre of the motor unit territory. Two subjects were used to test the method on forearm muscles, using two different approaches. With the first dataset, motor units were identified by decomposition of intramuscular EMG and the centre position of each motor unit territory was estimated from synchronized HDsEMG data. These positions were compared to the positions of the intramuscular fine wire electrodes with depth measured using ultrasound. With the second dataset, decomposition and motor unit localization was done directly on HDsEMG data, during specific muscle contractions. From the first dataset, the mean estimated depth of the motor unit centres were 8.7, 11.6, and 9.1 mm, with standard deviations 0.5, 0.1, and 1.3 mm, and the respective depths of the fine wire electrodes were 8.4, 15.8, and 9.1 mm. The second dataset generated distinct spatial distributions of motor unit activity which were used to identify the regions of different muscles of the forearm, in a 3-dimensional and projected 2-dimensional view. In conclusion, a method is presented which estimates motor unit centre positions from HDsEMG. The study demonstrates the shifting spatial distribution of muscle fibre activity between different efforts, which could be used to assess individual muscles on a motor unit level.

Design and Test of an Intraoral Electrode Grid for Tongue High-Density Electromyography.

Tongue motor function is crucial in a wide range of basic activities and its impairment affects quality of life. The electrophysiological assessment of the tongue relies primarily on needle electromyography, which is limited by its invasiveness and inability to capture the concurrent activity of the different tongue muscles. This work aimed at developing an intraoral grid for high-density surface electromyography (HDsEMG) to non-invasively map the electrical excitation of tongue muscles. We developed a grid of 4×8 electrodes deposited over an adhesive 8- μ m thick polyurethane membrane. The testing protocol was conducted on 7 healthy participants and included functional tasks (vowels articulation and tongue movements) aimed at activating different regions of the tongue. The electrical stability of contact was assessed by measuring electrode-tongue impedances before and after the tasks. The spatial amplitude distribution of global EMG and single motor unit action potentials (MUAPs) was characterized. Electrode-tongue impedance magnitude showed no significant changes in the pre-post comparison ( 58±46 k Ω vs. 67±58 k Ω at 50Hz). Contact stability was confirmed by the quality of the signals that allowed to quantify spatiotemporal characteristics of muscle activation during the different tasks. The analysis of the spatial distribution of individual MUAPs amplitude showed that they were confined to relatively small areas on the tongue surface (range: 0.5cm2 -3.9cm [Formula: see text]. A variety of different spatiotemporal MUAP patterns, likely due to the presence of different muscle compartments with different fiber orientations, were observed. Our results demonstrate that the developed electrode grid enables HDsEMG acquisition from the tongue during functional tasks, thus opening new possibilities in tongue muscle assessment both at global and single motor unit level.

Quantifying Muscle Co-activation for Impaired Finger Independence in Stroke Survivors.

Hand impairment frequently occurs in individuals following a stroke. There is evidence of abnormal muscle co-activation that contributes to impaired control of finger independence. This study quantitatively analyzed hand muscle co-activation patterns of chronic stroke survivors. Systematically quantifying the degree of muscle co-activation patterns in stroke survivors can help us to better understand the mechanisms behind compromised finger independence and enables a more accurate assessment of hand impairment. We analyzed muscle co-activation patterns both macroscopically and microscopically using high-density surface electromyographic (HD-sEMG) signals and decomposed motor unit signals from extrinsic and intrinsic flexor/extensor muscles. The muscle co-activation patterns between both sides of stroke survivors and neurologically intact controls were compared. We observed increased levels of co-activation in the affected sides of stroke survivors compared with their contralateral sides and the control groups, with a higher degree in the extrinsic muscles than the intrinsic muscles. The asymmetry in muscle co-activation between hands correlated with impaired finger force independence and clinical assessment scales. In the micro-level analysis of motor unit action potentials (MUAPs) distributions, we observed a notable increase in action potential spread of MUAPs in the individual affected extrinsic muscles, but the altered MUAP distribution did not correlate with clinical assessment scales. We systematically quantified abnormal muscle co-activation patterns in impaired finger independence after stroke. With further development, the outcomes provide a comprehensive understanding of hand dexterity deficits in stroke survivors, which may provide guidance for targeted rehabilitation strategies and offer a potential for automated impairment evaluations.

Prediction of Dexterous Finger Forces with Forearm Rotation using Motoneuron Discharges.

Motor unit (MU) discharge information obtained via electromyogram (EMG) decomposition can be used to decode dexterous multi-finger movement intention for neural-machine interfaces (NMI). However, the variation of the motor unit action potential (MUAP) shape resulted from forearm rotation leads to the decreased performance of EMG decomposition, especially under the real-time condition and then the degradation of motion decoding accuracy. The object of this study was to develop a method to realize the accurate extraction of MU discharge information across forearm pronated/supinated positions in the real-time condition for dexterous multi-finger force prediction. The FastICA-based EMG decomposition technique was used and the proposed method obtained multiple separation vectors for each MU at different forearm positions in the initialization phase. Under the real-time condition, the MU discharge information was extracted adaptively using the separation vector extracted at the nearest forearm position. As comparison, the previous method that utilized a single constant separation vector to extract MU discharges across forearm positions and the conventional method that utilized the EMG amplitude information were also performed. The results showed that the proposed method obtained a significantly better performance compared with the other two methods, manifested in a larger coefficient of determination (R2) and a smaller root mean squared error (RMSE) between the predicted and recorded force. Our results demonstrated the feasibility and the effectiveness of the proposed method to extract MU discharge information during forearm rotation for dexterous force prediction under the real-time conditions. Further development of the proposed method could potentially promote the application of the EMG decomposition technique for continuous dexterous motion decoding in a realistic NMI application scenario.

Integration of Motor Unit Filters for Enhanced Surface Electromyogram Decomposition During Varying Force Isometric Contraction.

Muscles generate varying levels of force by recruiting different numbers of motor units (MUs), and as the force increases, the number of recruited MUs gradually rises. However, current decoding methods encounter difficulties in maintaining a stable and consistent growth trend in MU numbers with increasing force. In some instances, an unexpected reduction in the number of MUs can even be observed as force intensifies. To address this issue, in this study, we propose an enhanced decoding method that adaptively reutilizes MU filters. Specifically, in addition to the normal decoding process, we introduced an additional procedure where MU filters are reused to initialize the algorithm. The MU filters are iterated and adapted to the new signals, aiming to decode motor units that were actually activated but cannot be identified due to heavy superimposition. We tested our method on both simulated and experimental surface electromyogram (sEMG) signals. We simulated isometric signals (10%-70%) with known MU firing patterns using experimentally recorded MU action potentials from forearm muscles and compared the decomposition results to two baseline approaches: convolution kernel compensation (CKC) and fast independent component analysis (fastICA). Our method increased the decoded MU number by a rate of 135.4% ± 62.5 % and 63.6% ± 20.2 % for CKC and fastICA, respectively, across different signal-to-noise ratios. The sensitivity and precision for MUs decomposed using the enhanced method remained at the same accuracy level (p <0.001) as those of normally decoded MUs. For the experimental signals, eight healthy subjects performed hand movements at five different force levels (10%-90%), during which sEMG signals were recorded and decomposed. The results indicate that the enhanced process increased the number of decoded MUs by 21.8% ± 10.9 % across all subjects. We discussed the possibility of fully capturing all activated motor units by appropriately reusing previously decoded MU filters and improving the balance of activated motor unit numbers across varying excitation levels.

Motor unit activity and synaptic inputs to motoneurons in the caudal part of the injured spinal cord.

Spinal cord injury (SCI) disrupts neuronal function below the lesion epicenter, causing disuse muscle atrophy. We investigated motor unit (MU) activity and synaptic inputs to motoneurons in the caudal region of the injured spinal cord. Participants with C4-C7 cervical injuries were studied. The extensor digitorum communis (EDC) muscle, which is mainly innervated by C8, was assessed for disuse muscle atrophy. Using advanced electromyography and signal-processing techniques, we examined the concurrent activation of a substantial population of MUs during force-tracking tasks. We found that in participants with SCI (n = 9), both MU discharge rates and the amplitudes of MU action potentials were significantly lower than in controls (n = 9). After SCI, MUs were recruited in a limited force range as the strength of muscle contractions increased, implying a disruption in the orderly MU recruitment pattern. Coherence analysis revealed reduced synaptic inputs to motoneurons in the delta band (0.5-5 Hz) for participants with SCI, suggesting diminished common synaptic inputs to the EDC muscle. In addition, participants with SCI exhibited greater muscle force variability. Using principal component analysis on low-frequency MU discharge rates, we found that the first common component (FCC) captured the most discharge variability in participants with SCI. The coefficients of variation (CV) of the FCC correlated with force signal CVs, suggesting force variability mainly results from common synaptic inputs to the EDC muscle after SCI. These results advance our understanding of the neurophysiology of disuse muscle atrophy in human SCI, paving the way for therapeutic interventions to restore muscle function.NEW & NOTEWORTHY This study analyzed motor unit (MU) function below the lesion epicenter in patients with spinal cord injury (SCI). We found reduced MU discharge rates and action potential amplitudes in participants with SCI compared with controls. The strength of common synaptic inputs to motoneurons was reduced in patients with SCI, with increased force variability primarily due to low-frequency oscillations of common inputs. This study enhances understanding of neurophysiological and behavioral changes in disuse muscle atrophy post-SCI.

Simulated Surface Electromyographic (SEMG) Signal Generation and Detection Model

Abstract For didactic purposes, the aim of this work was to improve a simulation model of surface electromyographic (sEMG) signal by taking into consideration the shortcomings of previously developed models. This model started with the simulation of the single fibre action potential (SFAP), then the model of the single motor unit action potential (MUAP), afterwards the imitation of the train of MUAP and finally the modellig of the resultant sEMG signal which is the sum of the MUAPs trains. SFAP simulation was based on: i) the description of the volume conductor model which is composed of four layers (bone, muscle, fat and skin), ii) the description of the electrodes shapes and sizes as well as spatial filters, iii) and the transmebrane current. The proposed model shows its effectiveness in the possibility of carrying out practical work by simulation on the modelling of SFAP, MUAP, MUAPT and the sEMG signal. The most important result of this model is that signal processing tools can be applied to analyze and interpret real-world phenomena such as the effects of physiological, non physiological and sensing system parameters on the shape of the simulated sEMG signal.

A computational model of surface electromyography signal alterations after spinal cord injury

Objective. Spinal cord injury (SCI) can cause significant impairment and disability with an impact on the quality of life for individuals with SCI and their caregivers. Surface electromyography (sEMG) is a sensitive and non-invasive technique to measure muscle activity and has demonstrated great potential in capturing neuromuscular changes resulting from SCI. The mechanisms of the sEMG signal characteristic changes due to SCI are multi-faceted and difficult to study in vivo. In this study, we utilized well-established computational models to characterize changes in sEMG signal after SCI and identify sEMG features that are sensitive and specific to different aspects of the SCI. Approach. Starting from existing models for motor neuron pool organization and motor unit action potential generation for healthy neuromuscular systems, we implemented scenarios to model damages to upper motor neurons, lower motor neurons, and the number of muscle fibers within each motor unit. After simulating sEMG signals from each scenario, we extracted time and frequency domain features and investigated the impact of SCI disruptions on sEMG features using the Kendall Rank Correlation analysis. Main results. The commonly used amplitude-based sEMG features (such as mean absolute values and root mean square) cannot differentiate between injury scenarios, but a broader set of features (including autoregression and cepstrum coefficients) provides greater specificity to the type of damage present. Significance. We introduce a novel approach to mechanistically relate sEMG features (often underused in SCI research) to different types of neuromuscular alterations that may occur after SCI. This work contributes to the further understanding and utilization of sEMG in clinical applications, which will ultimately improve patient outcomes after SCI.