Surface Electromyogram Recordings Research Articles

Cross-user gesture recognition from sEMG signals using an optimal transport assisted student-teacher framework

The cross-user gesture recognition is a puzzle in the myoelectric control system, owing to great variability in muscle activities across different users. To address this problem, a novel optimal transport (OT) assisted student-teacher (ST) framework (termed OT-ST) was proposed in this paper to facilitate transfer across user domains in an unsupervised domain adaptation (UDA) manner. In this framework, the initial parameters of the ST models were trained with the labeled data from users in the source domain. In the model transfer stage for a new user in the target domain, the teacher model was utilized to generate pseudo labels for unlabeled testing samples, providing guidance to the adaptation of the student model. The OT algorithm was employed to optimize the pseudo labels generated from the teacher model, avoiding the model bias and further improving the effect of domain adaptation. The performance of the proposed OT-ST framework was evaluated via experiments of classifying seven hand gestures using high-density surface electromyogram (HD-sEMG) recordings from extensor digitorum muscles of eight intact-limbed subjects. The OT-ST framework yielded a high accuracy of 96.50 ± 2.88% for new users, and outperformed other common machine learning and UDA methods significantly (p < 0.01), demonstrating its effectiveness. The OT-ST framework does not require special repetitive training or any labeled data for calibration. In addition, it can incrementally learn from new testing samples and improve the recognition ability. This study provides a promising method for developing user-generic myoelectric pattern recognition, with wide applications in human-computer interaction, consumer electronics and prosthesis control.

The role of neuromuscular function in sleep apnea pathogenesis and management: A consensus of experts

Study objectivesAlthough the importance of upper airway assessment in the consideration of obstructive sleep apnea (OSA) is recognized, there are current limitations in our approach to assessment.MethodsWe convened a group of experts in upper airway neuromuscular physiology and anatomy, sleep apnea endophenotypes, novel therapeutics and sleep epidemiology to summarize existing literature and delineate future opportunities to utilize and incorporate innovative and less invasive techniques focused on upper airway neuromuscular physiology to assess and manage OSA.ResultsIn OSA, genioglossus electromyogram (EMG) activity is reduced during sleep onset with higher levels observed during wakefulness compared to controls. Surface EMG recordings are limited due to distance from the actual muscle and while needle EMG offers more direct assessment, this approach is more invasive. Novel alternatives overcoming these limitations to assess upper airway neuromuscular physiology in OSA may therefore prove beneficial. Specifically, such an approach would facilitate identification of upstream prognostic biomarkers of OSA clinical trajectory and offer more informative mechanistic data. Novel approaches to neuromuscular assessment in OSA would enhance phenotyping to predict better tolerance to positive airway pressure therapy and set the stage to target neuromuscular function and upper airway anatomy. A quantifiable and repeatable neuromuscular physiologic metric has potential to facilitate a precision medicine strategy and personalize treatment, including measuring treatment response to neurophysiologic-focused interventions including hypoglossal nerve stimulation (HGNS), myofunctional therapy and neuromuscular electrical stimulation. A key area for future investigation is whether observed neuromuscular changes can identify patients at future risk of OSA, facilitating early intervention or prevention strategies.ConclusionsOverall, recognizing the critical contributions of abnormalities of upper airway neuromuscular function to the pathophysiology of OSA, it may be important to find accurate and reproducible neurophysiological assessments to address existing knowledge gaps in OSA assessment and management.

A Transformer-based Multi-task Learning Framework for Myoelectric Pattern Recognition Supporting Muscle Force Estimation.

Simultaneous implementation of myoelectric pattern recognition and muscle force estimation is highly demanded in building natural gestural interfaces but a challenging task due to the gesture classification accuracy degradation under varying muscle strengths. To address this problem, a novel method using transformer-based multi-task learning (MTL-Transformer) for the prediction of both myoelectric patterns and corresponding muscle strengths was proposed to describe the inherent characteristics of an individual gesture pattern under different force conditions, thereby improving the accuracy of myoelectric pattern recognition. In addition, the transformer model enabled the characterization of long-term temporal correlations to ensure precise and smooth estimation of the muscle force. The performance of the proposed MTL-Transformer framework was evaluated via experiments of classifying eleven hand gestures and estimating the corresponding muscle force simultaneously, using high-density surface electromyogram (HD-sEMG) recordings from forearm flexor muscles of eleven intact-limbed subjects. The MTL-Transformer framework yielded high classification accuracy (98.70±1.21%) and low root mean square deviation (12.59±2.76%), and outperformed other two common temporally modelling methods significantly (p < 0.05) in terms of both improved gesture recognition accuracies and reduced muscle force estimation errors. The MTL-Transformer framework is demonstrated as an effective solution for simultaneous implementation of myoelectric pattern recognition and muscle force estimation. This study promotes the development of robust and smooth myoelectric control systems, with wide applications in gestural interfaces, prosthetic and orthotic control.

Endurance-exercise training adaptations in spinal motoneurones: potential functional relevance to locomotor output and assessment in humans.

It is clear from non-human animal work that spinal motoneurones undergo endurance training (chronic) and locomotor (acute) related changes in their electrical properties and thus their ability to fire action potentials in response to synaptic input. The functional implications of these changes, however, are speculative. In humans, data suggests that similar chronic and acute changes in motoneurone excitability may occur, though the work is limited due to technical constraints. To examine the potential influence of chronic changes in human motoneurone excitability on the acute changes that occur during locomotor output, we must develop more sophisticated recording techniques or adapt our current methods. In this review, we briefly discuss chronic and acute changes in motoneurone excitability arising from non-human and human work. We then discuss the potential interaction effects of chronic and acute changes in motoneurone excitability and the potential impact on locomotor output. Finally, we discuss the use of high-density surface electromyogram recordings to examine human motor unit firing patterns and thus, indirectly, motoneurone excitability. The assessment of single motor units from high-density recording is mainly limited to tonic motor outputs and minimally dynamic motor output such as postural sway. Adapting this technology for use during locomotor outputs would allow us to gain a better understanding of the potential functional implications of endurance training-induced changes in human motoneurone excitability on motor output.

Immediate Effect of Anterior Cruciate Ligament Protective Knee Taping on Knee Landing Mechanics and Muscle Activations during Side Hops.

Athletic taping is widely used in sports to prevent injury. However, the effect of anterior cruciate ligament (ACL) protective taping on neuromuscular control during dynamic tasks remains unclear. Therefore, this study aimed to investigate the immediate effect of ACL protective taping on landing mechanics and muscle activations during side hops in healthy individuals. Fifteen healthy individuals (11 males and 4 females; age, 23.1 ± 1.4 years; height, 175.1 ± 10.4 cm; weight, 66.3 ± 11.2 kg) volunteered to participate in this study. Landing mechanics and muscle activations were measured while each participant performed single-leg hops side-to-side for ten repetitions with and without taping. An optical motion capture system and two force plates were used to collect the kinematic and kinetic data during the side hops. Surface electromyogram recordings were performed using a wireless electromyography system. Paired t-tests were performed to determine the differences in landing mechanics and muscle activations between the two conditions (taping and non-taping). The level of significance was set at p < 0.05. Compared with the non-taping condition, participants landed with a smaller knee abduction angle, greater knee external rotation angle, and smaller knee extensor moment in the taping condition. Given that greater knee abduction, internal rotation, and knee extension moment are associated with a greater risk of ACL injury, our findings suggest that ACL protective taping can have an immediate effect on dynamic knee stability. Clinicians should consider using ACL protective taping to facilitate the use of favorable landing mechanics for ACL injuries.

Decoding movement intent patterns based on spatiotemporal and adaptive filtering method towards active motor training in stroke rehabilitation systems

Upper extremity (UE) neuromuscular dysfunction critically affects post-stroke patients from performing activities of daily life. In this regard, various rehabilitation robotics have been developed for providing assistive and/or resistive forces that allow stroke survivors to train their arms towards regaining the lost arm function. However, most of the rehabilitation systems function in a passively such that they only allow patients navigate already-defined trajectories that often does not align with their UE movement intention, thus hindering adequate motor function recovery. One possible way to address this problem is to use a decoded UE motion intent to trigger active and intuitive motor training for the patients, which would help restore their UE arm functions. In this study, a new approach based on spatiotemporal neuromuscular descriptor and adaptive filtering technique (STD-AFT) is proposed to optimally characterize multiple patterns of UE movements in post-stroke patients towards providing inputs for intelligently driven motor training in the rehabilitation robotic systems. The proposed STD-AFT performance was systematically investigated and assessed in comparison with commonly adopted methods via high-density surface electromyogram recordings obtained from post-stroke survivors who performed 21 distinct classes of pre-defined limb movements. Furthermore, the movement intent decoding was done using four different classification algorithms. The experimental results showed that the proposed STD-AFT achieved significant improvement of up to 13.36% (p < 0.05) in characterizing the multiple patterns of movement intents with relatively lower standard-error value even in the presence of the external interference in form of noise compared to the existing benchmark methods. Also, the STD-AFT showed obvious pattern seperability for individual movement class in a two-dimensional space. The outcomes of this study suggest that the proposed STD-AFT could provide potential inputs for active and intuitive motor training in robotic systems targeted towards stroke-rehabilitation.

Open Access Dataset, Toolbox and Benchmark Processing Results of High-Density Surface Electromyogram Recordings.

We provide an open access dataset of High densitY Surface Electromyogram (HD-sEMG) Recordings (named "Hyser"), a toolbox for neural interface research, and benchmark results for pattern recognition and EMG-force applications. Data from 20 subjects were acquired twice per subject on different days following the same experimental paradigm. We acquired 256-channel HD-sEMG from forearm muscles during dexterous finger manipulations. This Hyser dataset contains five sub-datasets as: (1) pattern recognition (PR) dataset acquired during 34 commonly used hand gestures, (2) maximal voluntary muscle contraction (MVC) dataset while subjects contracted each individual finger, (3) one-degree of freedom (DoF) dataset acquired during force-varying contraction of each individual finger, (4) N-DoF dataset acquired during prescribed contractions of combinations of multiple fingers, and (5) random task dataset acquired during random contraction of combinations of fingers without any prescribed force trajectory. Dataset 1 can be used for gesture recognition studies. Datasets 2-5 also recorded individual finger forces, thus can be used for studies on proportional control of neuroprostheses. Our toolbox can be used to: (1) analyze each of the five datasets using standard benchmark methods and (2) decompose HD-sEMG signals into motor unit action potentials via independent component analysis. We expect our dataset, toolbox and benchmark analyses can provide a unique platform to promote a wide range of neural interface research and collaboration among neural rehabilitation engineers.

Performance Evaluation of a Wearable Tattoo Electrode Suitable for High-Resolution Surface Electromyogram Recording.

High-density surface electromyography (HD-sEMG) has been utilized extensively in neuromuscular research. Despite its potential advantages, limitations in electrode design have largely prevented widespread acceptance of the technology. Commercial electrodes have limited spatial fidelity, because of a lack of sharpness of the signal, and variable signal stability. We demonstrate here a novel tattoo electrode that addresses these issues. Our dry HD electrode grid exhibits remarkable deformability which ensures superior conformity with the skin surface, while faithfully recording signals during different levels of muscle contraction. We fabricated a 4 cm×3 cm tattoo HD electrode grid on a stretchable electronics membrane for sEMG applications. The grid was placed on the skin overlying the biceps brachii of healthy subjects, and was used to record signals for several hours while tracking different isometric contractions. The sEMG signals were recorded successfully from all 64 electrodes across the grid. These electrodes were able to faithfully record sEMG signals during repeated contractions while maintaining a stable baseline at rest. During voluntary contractions, broad EMG frequency content was preserved, with accurate reproduction of the EMG spectrum across the full signal bandwidth. The tattoo grid electrode can potentially be used for recording high-density sEMG from skin overlying major limb muscles. Layout programmability, good signal quality, excellent baseline stability, and easy wearability make this electrode a potentially valuable component of future HD electrode grid applications. The tattoo electrode can facilitate high fidelity recording in clinical applications such as tracking the evolution and time-course of challenging neuromuscular degenerative disorders.

Precise quantification of the time course of voluntary activation capacity following Botulinum toxin injections in the biceps brachii muscles of chronic stroke survivors

BackgroundSpasticity is a key motor impairment that affects many hemispheric stroke survivors. Intramuscular botulinum toxin (BT) injections are used widely to clinically manage spasticity-related symptoms in stroke survivors by chemically denervating muscle fibers from their associated motor neurons. In this study, we sought to understand how BT affects muscle activation, motor unit composition and voluntary force generating capacity over a time period of 3 months. Our purpose was to characterize the time course of functional changes in voluntary muscle activity in stroke survivors who are undergoing BT therapy as part of their physician-prescribed clinical plan.MethodOur assessment of the effects of BT was based on the quantification of surface electromyogram (sEMG) recordings in the biceps brachii (BB), an upper arm muscle and of voluntary contraction force. We report here on voluntary force and sEMG responses during isometric elbow contractions across consecutive recording sessions, spread over 12 weeks in three segments, starting with a preliminary session performed just prior to the BT injection. At predetermined time points, we conducted additional clinical assessments and we also recorded from the contralateral limbs of our stroke cohort. Eight subjects were studied for approximately 86 experimental recording sessions on both stroke-affected and contralateral sides.ResultsWe recorded an initial reduction in force and sEMG in all subjects, followed by a trajectory with a progressive return to baseline over a maximum of 12 weeks, although the minimum sEMG and minimum force were not always recorded at the same time point. Three participants were able to complete only one to two segments. Slope values of the sEMG-force relations were also found to vary across the different time segments. While sEMG-force slopes provide assessments of force generation capacity of the BT injected muscle, amplitude histograms from novel sEMG recordings during the voluntary tasks provide additional insights about differential actions of BT on the overall motor unit (MU) population over time.ConclusionsThe results of our study indicate that there are potential short term as well as long term decrements in muscle control and activation properties after BT administration on the affected side of chronic stroke survivors. Muscle activation levels as recorded using sEMG, did not routinely return to baseline even at three months’ post injection. The concurrent clinical measures also did not follow the same time course, nor did they provide the same resolution as our experimental measures. It follows that even 12 weeks after intramuscular BT injections muscle recovery may not be complete, and may thereby contribute to pre-existing paresis.

Continuous estimation of upper limb joint angle from sEMG signals based on SCA-LSTM deep learning approach

Robotic arm control has drawn a lot of attention along with the development of industrialization. The methods based on myoelectric pattern recognition have been proposed with multiple degrees of freedom for years. While these methods can support the actuation of several classes of discrete movements sequentially, they do not allow simultaneous control of multiple movements in a continuous manner like natural arms. In this study, we proposed a short connected autoencoder long short-term memory (SCA-LSTM) based simultaneous and proportional (SP) scheme that estimates continuous arm movements using kinematic information extracted from surface electromyogram (sEMG) recordings. The sEMG signals corresponding to seven classes of shoulder-elbow joint angle movements acquired from eleven participants were preprocessed using max root mean square envelope. Afterwards, the proposed SCA-LSTM model and two commonly applied models, namely, multilayer perceptrons (MLPs) and convolutional neural network (CNN), were trained and tested using the preprocessed data for continuous estimation of arm movements. Our experimental results showed that the proposed SCA-LSTM model could achieve a significantly higher estimation accuracy of approximately 95.7% that is consistently stable across the subjects in comparison to the CNN (86.8%) and MLP (83.4%) models. These results suggest that the proposed SCA-LSTM would be a promising model for continuous estimation of upper limb movements from sEMG signals for prosthetic control.

Characterizing Multiple Patterns of Motor Intent Using Spatial-Temporal Information for Intuitively Active Motor Training in Stroke Survivors.

Upper extremity motor function loss severely affects stroke survivors during daily life activities. Different rehabilitation robotic systems have been developed to allow stroke survivors regain their motor function. Meanwhile, most of the robots only operate in a passive mode and restrict the users to navigate predefined trajectories which may not align with their motion intent, thus limiting motor recovery. One way to resolve this issue would be to utilize a decoded movement intent to trigger intuitively active motor training for patients. In this direction, this study proposed and investigated the use of spatial-temporal neuromuscular descriptor (STD) for optimal decoding of multiple patterns of movement intents in patient to provide inputs for active motor training in the rehabilitation robotic systems. The STD performance was validated using High-Density surface electromyogram recordings from five stroke survivors who performed 21 limb movements. Experimental results show that the STD achieved a significant reduction in limb movement classification error (13.36%) even in the presence of the inevitable White Gaussian Noise compared to other methods (p<0.05). The STD also showed obvious class separability for individual movement. Findings from this study suggest that the STD may provide potential inputs for intuitively active motor training in stroke rehabilitation robotic systems.Clinical Relevance- This study showed that spatial-temporal neuromuscular information could aid adequate decoding of movement intents upon which intuitively active motor training could be achieved in stroke rehabilitation robotic systems.

Wrestle while you learn: EMG as a teaching tool for undergraduate skeletal muscle physiology teaching.

Wrestle while you learn: EMG as a teaching tool for undergraduate skeletal muscle physiology teaching.

Multimodal stimuli modulate rapid visual responses during reaching.

The reticulospinal tract plays an important role in primate upper limb function, but methods for assessing its activity are limited. One promising approach is to measure rapid visual responses (RVRs) in arm muscle activity during a visually cued reaching task; these may arise from a tecto-reticulospinal pathway. We investigated whether changes in reticulospinal excitability can be assessed noninvasively using RVRs, by pairing the visual stimuli of the reaching task with electrical stimulation of the median nerve, galvanic vestibular stimulation, or loud sounds, all of which are known to activate the reticular formation. Surface electromyogram (EMG) recordings were made from the right deltoid of healthy human subjects as they performed fast reaching movements toward visual targets. Stimuli were delivered up to 200 ms before target appearance, and RVR was quantified as the EMG amplitude in a window 75-125 ms after visual target onset. Median nerve, vestibular, and auditory stimuli all consistently facilitated the RVRs, as well as reducing the latency of responses. We propose that this facilitation reflects modulation of tecto-reticulospinal excitability, which is consistent with the idea that the amplitude of RVRs can be used to assess changes in brain stem excitability noninvasively in humans.NEW & NOTEWORTHY Short-latency responses in arm muscles evoked during a visually driven reaching task have previously been proposed to be tecto-reticulospinal in origin. We demonstrate that these responses can be facilitated by pairing the appearance of a visual target with stimuli that activate the reticular formation: median nerve, vestibular, and auditory stimuli. We propose that this reflects noninvasive measurement and modulation of reticulospinal excitability.

Visualized Evidences for Detecting Novelty in Myoelectric Pattern Recognition using 3D Convolutional Neural Networks.

Although myoelectric pattern recognition (MPR) has been considered as a milestone technique to enable dexterous control of multiple degrees of freedom, outlier data interference (i.e., novelty) is a big issue affecting stability of conventional MPR control and its wide applications. Inspired by video classification techniques, we propose a novel method using 3-dimensional (3D) convolutional neural network (CNN) for extracting sufficient spatial-temporal features from high-density surface electromyogram (EMG) recordings processed as a video stream where time-varying information of muscular activity was taken into account. Given the targeted task patterns accurately characterized by the proposed method, it is straightforward to discriminate and then reject outlier data interferences regarded as untargeted and unlearnt patterns. Meanwhile, the strength of the 3D CNN in discriminating targeted task patterns through spatial-temporal features is further visualized and confirmed by t-Distributed Stochastic Neighbor Embedding (t-SNE). The performance of the proposed method was evaluated with surface EMG recorded by two 6 × 8 electrode arrays placed over forearm flexors and extensors of 3 subjects performing 7 targeted and 4 outlier motions. The visualized results by t-SNE showed the targeted task patterns were separated with each concentrated into a small region, while the outlier patterns were scattered around. On this basis, a traditional Mahalanobis Distance (MD) based method was applied for novelty detection and targeted task classification. Finally, the proposed method yielded averaged error rate of 10.98% for the targeted task patterns and <; 5% for all outlier data, respectively, which outperformed a common baseline method. All these findings demonstrate the advantage of characterizing myoelectric patterns using the proposed method and the potential of applying it to reject outlier data interference.

Spatio-Temporal Based Descriptor for Limb Movement-Intent Characterization in EMG-Pattern Recognition System.

Electromyogram (EMG) pattern-recognition (PR) is the most widely adopted prostheses/rehabilitation robots control method that seamlessly support multi-degrees of freedom (MDF) function in an intuitive fashion. The feature extraction framework applied in such PR-based control essentially determines the control performance of the prosthetic device. Based on the drawbacks of the commonly utilized feature extraction methods, this study proposed a spatio-temporal-based feature set (STFS) that might optimally characterize EMG signal patterns even in the presence of white Gaussian noise (WGN) to realize consistently accurate and stable decoding of multiple classes of limb-movements. For benchmark evaluation, the performance of the proposed STFS method was examined in comparison to notable existing popular methods using high density surface EMG recordings from 8 amputees, with metrics such as classification error (CE) and feature-space separability index. Compared to the existing methods, the STFS recorded substantial reduction of up 16.73% even in the presence the inevitable WGN at p<; 0.05. Also, with principal component analysis concept, the proposed STFS feature-space indicates obvious class separability compared to the previous methods. Therefore, the newly proposed STFS method could potentially facilitate the realization of consistently accurate and reliable PR-based control for MDF prostheses/rehabilitation robots.

Two-Source Validation of Progressive FastICA Peel-Off for Automatic Surface EMG Decomposition in Human First Dorsal Interosseous Muscle.

This study aims to assess the accuracy of a novel high density surface electromyogram (SEMG) decomposition method, namely automatic progressive FastICA peel-off (APFP), for automatic decomposition of experimental electrode array SEMG signals. A two-source method was performed by simultaneous concentric needle EMG and electrode array SEMG recordings from the human first dorsal interosseous (FDI) muscle, using a protocol commonly applied in clinical EMG examination. The electrode array SEMG was automatically decomposed by the APFP while the motor unit action potential (MUAP) trains were also independently identified from the concentric needle EMG. The degree of agreement of the common motor unit (MU) discharge timings decomposed from the two different categories of EMG signals was assessed. A total of 861 and 217 MUs were identified from the 114 trials of simultaneous high density SEMG and concentric needle EMG recordings, respectively. Among them 168 common (MUs) were found with a high average matching rate of [Formula: see text] for the discharge timings. The outcomes of this study show that the APFP can reliably decompose at least a subset of MUs in the high density SEMG signals recorded from the human FDI muscle during low contraction levels using a protocol analog to clinical EMG examination.

Innervation asymmetry of the external anal sphincter in aging characterized from high-density intra-rectal surface EMG recordings.

The innervation zone asymmetry of the external anal sphincter (EAS) has been investigated as a risk factor for the development of fecal incontinence (FI). This study aims to utilize an intra-rectal high-density surface electromyogram (HD-sEMG) recording and advanced HD-sEMG decomposition technique to characterize the effects of aging on the asymmetry of EAS functional innervation. HD-sEMG signals were recorded intra-rectally from six healthy young and seven healthy elderly women during voluntary contractions of the EAS. EMG signals were decomposed into constituent motor unit action potentials (MUAPs) and the innervation zone of each decomposed motor unit was identified. Asymmetry index (AI) was defined and calculated for all subjects. The maximum squeezing pressures of the EAS were also measured for all subjects as a comparison. The HD-sEMG decomposition and AI calculation were successfully performed from EMG data acquired from all the subjects. The AI values were 28.7 ± 17.0% for the young group and 55.6 ± 18.8% for the elderly group. The AI and EAS contraction strength were found to be negatively correlated (P < 0.05). A two-tailed student's t-test demonstrated a significant increase in AI with age by comparison between two groups (P < 0.05). Our work demonstrates, for the first time, that EAS functional innervation tends to become increasingly asymmetrical with advancing age, and this increase is associated with a compromised anorectal function. Results suggest that the intra-rectal HD-sEMG will serve as an advanced tool for assessing and monitoring the anorectal neuromuscular function minimally invasively under different pathophysiological conditions.

Higher muscle fiber conduction velocity and early rate of torque development in chronically strength-trained individuals.

Strength-trained individuals (ST) develop greater levels of force compared with untrained subjects. These differences are partly of neural origin and can be explained by training-induced changes in the neural drive to the muscles. In the present study we hypothesize a greater rate of torque development (RTD) and faster recruitment of motor units with greater muscle fiber conduction velocity (MFCV) in ST compared with a control cohort. MFCV was assessed during maximal voluntary isometric explosive contractions of the elbow flexors in eight ST and eight control individuals. MFCV was estimated from high-density surface electromyogram recordings (128 electrodes) in intervals of 50 ms starting from the onset of the electromyogram. RTD and MFCV were computed and normalized to their maximal voluntary torque (MVT) values. The explosive torque of the ST was greater than in the control group in all time intervals analyzed (P < 0.001). The absolute MFCV values were also greater for the ST than for controls at all time intervals (P < 0.001). ST also achieved greater normalized RTD in the first 50 ms of contraction [887.6 (152) vs. 568.5 (148.66)%MVT/s, mean (SD), P < 0.001] and normalized MFCV before the rise in force compared with controls. We have shown for the first time that ST can recruit motor units with greater MFCV in a shorter amount of time compared with untrained subjects during maximal voluntary isometric explosive contractions.NEW & NOTEWORTHY Strength-trained individuals show neuromuscular adaptations. These adaptations have been partly related to changes in the neural drive to the muscles. Here, we show for the first time that during the initial phase of a maximal isometric explosive contraction, strength-trained individuals achieve higher levels of force and recruit motor units with greater conduction velocities.

F59. Spasmodic reflex myoclonus-an characteristic electrophysiological manifestation for the diagnosis of Stiff-person syndrome

Introduction Stiff person syndrome (SPS) is a rare disease entity and the pivotal clinical features contain progressive course of fluctuating stiffness of the low back and lower limbs. Progressive encephalomyelitis with rigidity and myoclonus (PERM) is one of the unique variants of stiff person syndrome, and it was characterized with rigidity, stimulus-sensitive spasms, myoclonus, autonomic disturbance, and brainstem signs. These features resemble those of hyperekplexia. The accurate diagnosis of SPS is difficult but is crucial for the in time intervention with immunotherapy for life saving. In addition to the detection of responsible antibody, clinical electrophysiological manifestation, spasmodic reflex myoclonus, would be of great help in establishing the diagnosis. Methods Multichannel surface electromyogram (sEMG) and accelerometer recordings were conducted over the lumbar paraspinalis, rectus abdominalis, quadriceps, and hamstring muscles. Stimulus sensitive phenomenon was illustrated with accelerometer tapped over the pinprick. The signals were processed and digitized with CED power1401 with Spike2. The band pass for sEMG was set from 80 Hz to 1000 Hz. Results The results demonstrated that episodes of spontaneous onset of spasm of the low trunk and legs lasted about seconds to minutes. Stimuli with nose tip tapping, would elicit vigorous jerks with dissemination of long lasting muscle spasm of the neck, paraspinal muscle, rectus abdominalis muscle, to quadriceps and hamstring muscles in a descending spreading pattern. The unique electrophysiological manifestations illustrate the characteristic feature of spasmodic reflex myoclonus. The surface EMG recording also showed the phenomena of habituation, in which a trend of amplitude decrescendo from the first toward the third EMG responses were observed during consecutive auditory stimulus. The serum levels of anti-glutamic acid decarboxylase 65 antibodies was 164 IU. Conclusion The unique EMG pattern can help identify SPS. The clinical features of spasmodic reflex myoclonus are similar to hyperekplexia. However, there are some differences between the two phenomena. Unlike hyperekplexia, the spasmodic reflex myoclonus can be habituated after repetitive stimulation in the SPS patient. Furthermore, the initial myoclonic bursts of spasmodic reflex myoclonus were not present in hyperekplexia and the EMG burst duration was much shorter of the hyperekplexia as compared with that of spasmodic reflex myoclonus. These neurophysiological features discern spasmodic reflex myoclonus from hyperekplexia and are helpful in making diagnosis of SPS.

A Robust Sparse Representation Based Pattern Recognition Approach for Myoelectric Control

Interferences in the form of white Gaussian noise (WGN) are inevitable during long-term electromyogram (EMG) recordings. Even with the aid of advanced signal denoising techniques, such an intermittent interference is hardly detected and attenuated in the practical use of EMG-driven control systems. Hence, a robust pattern recognition scheme that is invariant to noise contamination would potentially aid the realization of an efficient EMG-based pattern recognition (EMG-PR) control system. To this end, an EMG-PR scheme driven by sparse representation-based classification (SRC) algorithm and root mean square (rms) descriptor (RMS-SRC) is proposed in this paper. The accuracy and the robustness of the proposed scheme were investigated using the high-density surface EMG recordings from 12 traumatic braininjured patients and 5 post-stroke survivors. For benchmark comparison, another three different feature sets and four pattern recognition algorithms were considered. The optimal pattern recognition schemes with respect to each feature-classifier combination were first selected in the absence of WGN contamination. Then, six levels of WGN with a signal-to-noise ratio (SNR) ranging from 5 to 30 dB were added into the EMG recordings, respectively, to mimic the different WGN interferences. Our result showed that the proposed RMS-SRC scheme could achieve a similar accuracy with the benchmark schemes in the presence of limited noise contamination (0-15 dB), and significantly outperformed the other schemes when the SNR of WGN increased (20-30 dB). More notably, the RMS-SRC scheme significantly outperformed the other pattern recognition schemes when the WGN existed in either training set or testing set only. The findings proved the comparative advantage of the proposed RMS-SRC pattern recognition scheme over the other currently used schemes in the myoelectric control. Thus, the proposed scheme would potentially facilitate the development of EMG-driven rehabilitation robots for accurate and dexterous assistive training for patients with neurological disorders.