Unsupervised neural decoding for concurrent and continuous multi-finger force prediction

Reliable prediction of multi-finger forces is crucial for neural-machine interfaces. Various neural decoding methods have progressed substantially for accurate motor output predictions. However, most neural decoding methods are performed in a supervised manner, i.e., the finger forces are needed for model training, which may not be suitable in certain contexts, especially in scenarios involving individuals with an arm amputation. To address this issue, we developed an unsupervised neural decoding approach to predict multi-finger forces using spinal motoneuron firing information. We acquired high-density surface electromyogram (sEMG) signals of the finger extensor muscle when subjects performed single-finger and multi-finger tasks of isometric extensions. We first extracted motor units (MUs) from sEMG signals of the single-finger tasks. Because of inevitable finger muscle co-activation, MUs controlling the non-targeted fingers can also be recruited. To ensure an accurate finger force prediction, these MUs need to be teased out. To this end, we clustered the decomposed MUs based on inter-MU distances measured by the dynamic time warping technique, and we then labeled the MUs using the mean firing rate or the firing rate phase amplitude. We merged the clustered MUs related to the same target finger and assigned weights based on the consistency of the MUs being retained. As a result, compared with the supervised neural decoding approach and the conventional sEMG amplitude approach, our new approach can achieve a higher R2 (0.77 ± 0.036 vs. 0.71 ± 0.11 vs. 0.61 ± 0.09) and a lower root mean square error (5.16 ± 0.58 %MVC vs. 5.88 ± 1.34 %MVC vs. 7.56 ± 1.60 %MVC). Our findings can pave the way for the development of accurate and robust neural-machine interfaces, which can significantly enhance the experience during human-robotic hand interactions in diverse contexts.

Accurate Identification of Motoneuron Discharges From Ultrasound Images Across the Full Muscle Cross-Section

OBJECTIVE

Non-invasive identification of motoneuron (MN) activity commonly uses electromyography (EMG). However, surface EMG (sEMG) detects only superficial sources, at less than approximately 10-mm depth. Intramuscular EMG can detect deep sources, but it is limited to sources within a few mm of the detection site. Conversely, ultrasound (US) images have high spatial resolution across the whole muscle cross-section. The activity of MNs can be extracted from US images due to the movements that MN activation generates in the innervated muscle fibers. Current US-based decomposition methods can accurately identify the location and average twitch induced by MN activity. However, they cannot accurately detect MN discharge times.

METHODS

Here, we present a method based on the convolutive blind source separation of US images to estimate MN discharge times with high accuracy. The method was validated across Ten participants using concomitant sEMG decomposition as the ground truth.

RESULTS

140 unique MN spike trains were identified from US images, with a rate of agreement (RoA) with sEMG decomposition of 87.4 ± 10.3%. Over 50% of these MN spike trains had a RoA greater than 90%. Furthermore, with US, we identified additional MUs well beyond the sEMG detection volume, at up to >30 mm below the skin.

CONCLUSION

The proposed method can identify discharges of MNs innervating muscle fibers in a large range of depths within the muscle from US images.

SIGNIFICANCE

The proposed methodology can non-invasively interface with the outer layers of the central nervous system innervating muscles across the full cross-section.

Online Muscle Activation Onset Detection Using Likelihood of Conditional Heteroskedasticity of Electromyography Signals

Surface electromyography (sEMG) signals are crucial in developing human-machine interfaces, as they contain rich information about human neuromuscular activities.

OBJECTIVE

The real-time, accurate detection of muscle activation onset (MAO) is significant for EMG-triggered control strategies in embedded applications like prostheses and exoskeletons.

METHODS

This paper investigates sEMG signals using the generalized autoregressive conditional heteroskedasticity (GARCH) model, focusing on variance. A novel feature, the likelihood of conditional heteroskedasticity (LCH) extracted from the maximum likelihood estimation of GARCH parameters, is proposed. This feature effectively distinguishes signal from noise based on heteroskedasticity, allowing for the detection of MAO through the LCH feature and a basic threshold classifier. For online calculation, the model parameter estimation is simplified, enabling direct calculation of the LCH value using fixed parameters.

RESULTS

The proposed method was validated on two open-source datasets and demonstrated superior performance over existing methods. The mean absolute error of onset detection, compared with visual detection results, is approximately 65 ms under online conditions, showcasing high accuracy, universality, and noise insensitivity.

CONCLUSION

The results indicate that the proposed method using the LCH feature from the GARCH model is highly effective for real-time detection of muscle activation onset in sEMG signals.

SIGNIFICANCE

This novel approach shows great potential and possibility for real-world applications, reflecting its superior performance in accuracy, universality, and insensitivity to noise.

[Practical instructions for recording vestibular evoked myogenic potentials]

Recording of vestibular evoked myogenic potentials (VEMPs) is a well-established method for functional diagnostics of the otolith organs. VEMPs are vestibular reflexes of the sacculus und utriculus to acoustic stimulation by air-conducted sound or bone-conducted vibration and are recorded by surface electrodes from the cervical (cVEMP) and ocular (oVEMP) muscles. The results of VEMP recordings are part of the neuro-otologic test battery and enable diagnosis of various vestibular disorders or differentiation between non-vestibular and peripheral vestibular vertigo. However, the methods for recording VEMPs vary substantially, although recording and stimulation parameters as well as methods of data analysis have a significant influence on the results. This article provides an overview of recommended parameters as well as practical instructions for the recording, analysis, and interpretation of VEMPs.

The influence of chair recline and head and neck position on upper trapezius activity and stiffness during seated computer work

Increasing chair recline during seated computer work may reduce the load placed on the upper trapezius (UT), a common location of pain for those with idiopathic chronic neck pain. This study determined the effect of increasing chair recline on UT stiffness and muscle activity during computer work in people with and without idiopathic chronic neck pain. Surface electromyography and ultrasound shear wave elastography were collected from three subdivisions of the UT in 15 individuals with idiopathic chronic neck pain and 15 sex-matched healthy controls. Participants sat in a standardized computer-work setup while chair recline (0°, 25°, 45°) and head and neck position (self-selected, neutral, flexed) were systematically adjusted and maintained for 2.5-min intervals. Repeated-measures ANOVAs were completed for each sex, muscle, and data type, with group (chronic neck pain, control), chair recline (0°,25°,45°), head and neck position (self-selected, flexed, neutral), and side of collected data (dominant, non-dominant) as fixed factors. Men with idiopathic chronic neck pain demonstrated greater UT stiffness in the cranial subdivision when compared to healthy men. Additionally, the 25° and 45° recline levels increased the stiffness of men's dominant UT compared to men's non-dominant UT. Women's UT was more affected by head and neck position, and a neutral head and neck position resulted in lower UT activation, but higher UT stiffness for the cranial subdivision and midway between C-7 and the acromion process. Overall, our findings suggest that the commonly suggested neutral position may not be a beneficial prompt when positioning someone during seated computer work.

Surface electromyography for evaluating patients with oromandibular dystonia

OBJECTIVE

To investigate myoelectric signals of dystonic activities in oromandibular dystonia (OMD) subjects using surface electromyography (EMG).

METHODS

Twelve OMD subjects were included in this study. Resting myoelectric activities of the superficial masseter, anterior temporalis, and anterior belly of the digastric muscle on both sides were monitored, and dystonic muscle contractions were recorded using surface EMG. Myoelectric signal amplitude, the type of muscle contraction, and contraction rate for phasic activities were evaluated.

RESULTS

Surface EMG revealed that eight subjects had dystonic muscle activities in the phasic contraction pattern, three subjects had a tonic contraction pattern, and one subject had a mixed pattern. Synchronous contraction of dystonic muscles was frequently observed. Many of the monitored muscles showed high resting amplitudes.

CONCLUSION

Surface EMG detects abnormal muscle activities related to oromandibular dystonia. Surface EMG can serve as an objective method for diagnosing oromandibular dystonia.

A survey on the state of the art of force myography technique (FMG): analysis and assessment

Precise feedback assures precise control commands especially for assistive or rehabilitation devices. Biofeedback systems integrated with assistive or rehabilitative robotic exoskeletons tend to increase its performance and effectiveness. Therefore, there has been plenty of research in the field of biofeedback covering different aspects such as signal acquisition, conditioning, feature extraction and integration with the control system. Among several types of biofeedback systems, Force myography (FMG) technique is a promising one in terms of affordability, high classification accuracies, ease to use, and low computational cost. Compared to traditional biofeedback systems such as electromyography (EMG) which offers some invasive techniques, FMG offers a completely non-invasive solution with much less effort for preprocessing with high accuracies. This work covers the whole aspects of FMG technique in terms of signal acquisition, feature extraction, signal processing, developing the machine learning model, evaluating tools for the performance of the model. Stating the difference between real-time and offline assessment, also highlighting the main uncovered points for further study, and thus enhancing the development of this technique.

Evaluation of antigravitational support levels provided by a passive upper-limb occupational exoskeleton in repetitive arm movements

Upper-limb occupational exoskeletons to support the workers' upper arms are typically designed to provide antigravitational support. Although typical work activities require workers to perform static and dynamic actions, the majority of the studies in literature investigated the effects of upper-limb occupational exoskeletons in static and quasi-static activities, while only a few works focused on dynamic tasks. This article presents a systematic evaluation of the effects of different levels of antigravitational support (from about 60% to 100% of the arm gravitational load) provided by a passive upper-limb occupational exoskeleton on muscles' activity during repetitive arm movements. The effect of the exoskeleton on muscle activity was evaluated by the comparison of muscle activations with and without the exoskeleton. The average muscle activation was computed considering shoulder full flexion-extension cycles, and sub-movements, namely the arm-lifting (i.e., flexion) and arm-lowering (i.e., extension) movements. Results showed a quasi-linear correlation between antigravitational support and muscle activity reductions, both when considering the full flexion-extension cycle and in the arm-lifting movement (reductions were up to 64 and 61% compared to not wearing the exoskeleton, respectively). When considering the arm-lowering movement, providing antigravitational support close to or higher than 100% of the arm gravitational load led to increased muscle activations of the extensors (up to 127%), suggesting that such an amount of antigravitational support may be not effective for a complete biomechanical load reduction on the shoulder district in dynamic tasks.

Mapping lumbar efferent and afferent spinal circuitries via paddle array in a porcine model

BACKGROUND

Preclinical models are essential for identifying changes occurring after neurologic injury and assessing therapeutic interventions. Yucatan miniature pigs (minipigs) have brain and spinal cord dimensions like humans and are useful for laboratory-to-clinic studies. Yet, little work has been done to map spinal sensorimotor distributions and identify similarities and differences between the porcine and human spinal cords.

NEW METHODS

To characterize efferent and afferent signaling, we implanted a conventional 32-contact, four-column array into the dorsal epidural space over the lumbosacral spinal cord, spanning the L5-L6 vertebrae, in two Yucatan minipigs. Spinally evoked motor potentials were recorded bilaterally in four hindlimb muscles during stimulation delivered from different array locations. Then, cord dorsum potentials were recorded via the array by stimulating the left and right tibial nerves.

RESULTS

Utilizing epidural spinal stimulation, we achieved selective left, right, proximal, and distal activation in the hindlimb muscles. We then determined the selectivity of each muscle as a function of stimulation location which relates to the distribution of the lumbar motor pools.

COMPARISON WITH EXISTING METHODS

Mapping motoneuron distribution to hindlimb muscles and recording responses to peripheral nerve stimulation in the dorsal epidural space reveals insights into ascending and descending signal propagation in the lumbar spinal cord. Clinical-grade arrays have not been utilized in a porcine model.

CONCLUSIONS

These results indicate that efferent and afferent spinal sensorimotor networks are spatially distinct, provide information about the organization of motor pools in the lumbar enlargement, and demonstrate the feasibility of using clinical-grade devices in large animal research.

Quantitative Assessment via Multi-Domain Fusion of Muscle Synergy Associated With Upper-Limb Motor Function for Stroke Rehabilitation

Quantitative assessment of upper limb motor function aids therapists in providing appropriate rehabilitation strategies, which plays an essential role in post-stroke rehabilitation. Traditional assessments, relying on clinical scales or kinematic metrics, often involve subjective scores or are influenced by compensatory strategies. Recently, the use of muscle synergies, representing simplified neuromuscular control, has emerged as a promising approach for post-stroke assessment. In general, muscle synergies are decomposed into two components: synergy vectors and synergy activation. Synergy vectors represent the relative weighting of each muscle within each synergy, that is muscle coordination; synergy activation represents the recruitment of the muscle synergy over time, that is muscle activation strength. Both components are vital for adequately assessing patients' motor function. Therefore, we integrate the spatial domain and temporal domain features extracted from synergy vectors and synergy activation, constructing a multi-domain assessment system using a Random Forest classifier, which may provide great qualitative classification accuracy. Furthermore, a novel functional score is generated from the probabilities belonging to the pathological group. Finally, A study involving ten healthy subjects and ten post-stroke patients validates the proposed method. The experimental results show that the classification accuracy was enhanced to 98.56% by fusing the characteristics derived from different domains, which was higher than that based on spatial domain (94.90%) and temporal domain (91.08%), respectively. Furthermore, the assessment score generated by multi-domain fusion framework exhibited a significant correlation with the clinical score. These promising results show the potential of applying the proposed method to clinical assessments for post-stroke patients.

Developing RPC-Net: Leveraging High-Density Electromyography and Machine Learning for Improved Hand Position Estimation

OBJECTIVE

The purpose of this study was to develop and evaluate the performance of RPC-Net (Recursive Prosthetic Control Network), a novel method using simple neural network architectures to translate electromyographic activity into hand position with high accuracy and computational efficiency.

METHODS

RPC-Net uses a regression-based approach to convert forearm electromyographic signals into hand kinematics. We tested the adaptability of the algorithm to different conditions and compared its performance with that of solutions from the academic literature.

RESULTS

RPC-Net demonstrated a high degree of accuracy in predicting hand position from electromyographic activity, outperforming other solutions with the same computational cost. Including previous position data consistently improved results across subjects and conditions. RPC-Net showed robustness against a reduction in the number of electromyography electrodes used and shorter input signals, indicating potential for further reduction in computational cost.

CONCLUSION

The results demonstrate that RPC-Net is capable of accurately translating forearm electromyographic activity into hand position, offering a practical and adaptable tool that may be accessible in clinical settings.

SIGNIFICANCE

The development of RPC-Net represents a significant advancement. In clinical settings, its application could enable prosthetic devices to be controlled in a way that feels more natural, improving the quality of life for individuals with limb loss.

Skeletal muscle fiber type and TMS-induced muscle relaxation in unfatigued and fatigued knee-extensor muscles

The force drop after transcranial magnetic stimulation (TMS) delivered to the motor cortex during voluntary muscle contractions could inform about muscle relaxation properties. Because of the physiological relation between skeletal muscle fiber-type distribution and size and muscle relaxation, TMS could be a noninvasive index of muscle relaxation in humans. By combining a noninvasive technique to record muscle relaxation in vivo (TMS) with the gold standard technique for muscle tissue sampling (muscle biopsy), we investigated the relation between TMS-induced muscle relaxation in unfatigued and fatigued states, and muscle fiber-type distribution and size. Sixteen participants (7F/9M) volunteered to participate. Maximal knee-extensor voluntary isometric contractions were performed with TMS before and after a 2-min sustained maximal voluntary isometric contraction. Vastus lateralis muscle tissue was obtained separately from the participants' dominant limb. Fiber type I distribution and relative cross-sectional area of fiber type I correlated with TMS-induced muscle relaxation at baseline (r = 0.67, adjusted P = 0.01; r = 0.74, adjusted P = 0.004, respectively) and normalized TMS-induced muscle relaxation as a percentage of baseline (r = 0.50, adjusted P = 0.049; r = 0.56, adjusted P = 0.031, respectively). The variance in the normalized peak relaxation rate at baseline (59.8%, P < 0.001) and in the fatigue resistance (23.0%, P = 0.035) were explained by the relative cross-sectional area of fiber type I to total fiber area. Fiber type I proportional area influences TMS-induced muscle relaxation, suggesting TMS as an alternative method to noninvasively inform about skeletal muscle relaxation properties.NEW & NOTEWORTHY Transcranial magnetic stimulation (TMS)-induced muscle relaxation reflects intrinsic muscle contractile properties by interrupting the drive from the central nervous system during voluntary muscle contractions. We showed that fiber type I proportional area influences the TMS-induced muscle relaxation, suggesting that TMS could be used for the noninvasive estimation of muscle relaxation in unfatigued and fatigued human muscles when the feasibility of more direct method to study relaxation properties (i.e., muscle biopsy) is restricted.

Modulation of intracortical circuits in primary motor cortex during automatic action tendencies

Humans display automatic action tendencies toward emotional stimuli, showing faster automatic behavior (i.e., approaching a positive stimulus and avoiding a negative stimulus) than regulated behavior (i.e., avoiding a positive stimulus and approaching a negative stimulus). Previous studies have shown that the primary motor cortex is involved in the processing of automatic actions, with higher motor evoked potential amplitudes during automatic behavior elicited by single-pulse transcranial magnetic stimulation. However, it is unknown how intracortical circuits are involved with automatic action tendencies. Here, we measured short-interval intracortical inhibition and intracortical facilitation within the primary motor cortex by using paired-pulse transcranial magnetic stimulation protocols during a manikin task, which has been widely used to explore approaching and avoiding behavior. Results showed that intracortical facilitation was stronger during automatic behavior than during regulated behavior. Moreover, there was a significant negative correlation between reaction times and intracortical facilitation effect during automatic behavior: individuals with short reaction times had stronger faciliatory activity, as shown by higher intracortical facilitation. By contrast, no significant difference was found for short-interval intracortical inhibition between automatic behavior and regulated behavior. The results indicated that the intracortical facilitation circuit, mediated by excitatory glutamatergic neurons, in the primary motor cortex, plays an important role in mediating automatic action tendencies. This finding further supports the link between emotional perception and the action system.

Delay estimation for cortical-muscular interaction with wavelet coherence time lag

BACKGROUND

Cortico-muscular coherence (CMC) between the cerebral cortex and muscle activity is an effective tool for studying neural communication in the motor control system. To accurately evaluate the coherence between electroencephalogram (EEG) and electromyogram (EMG) signals, it is necessary to accurately calculate the time delay between physiological signals to ensure signal synchronization.

NEW METHOD

We proposed a new delay estimation method, named wavelet coherence time lag (WCTL) and the significant increase areas (SIA) index as a measure of the specific region enhancement effect of the magnitude squared coherence (MSC) image.

RESULTS

The grip strength level had a small effect on the information transmission time from the cortex to the muscles, while the transmission time from the cortex to different muscle channels was different for the same task. A positive correlation was found between the grip strength level and the SIA index on the β band of C3-B and the α and β bands of C3-FDS.

COMPARISON WITH EXISTING METHOD

The WCTL method was found to accurately calculate the delay time even when the number of repeated segments was low in a simple motor control model, and the results were more accurate than the rate of voxels change (RVC) and CMC with time lag (CMCTL) methods.

CONCLUSIONS

The WCTL is an effective method for detecting the transmission time of information between the cortex and muscles, laying the foundation for future rehabilitation treatment for stroke patients.

Cadence Modulation during Eccentric Cycling Affects Perception of Effort But Not Neuromuscular Alterations

INTRODUCTION

A recent study showed that cadence modulation during short eccentric cycling exercise affects oxygen consumption (V̇O 2 ), muscular activity (EMG), and perception of effort (PE). This study examined the effect of cadence on V̇O 2 , EMG, and PE during prolonged eccentric cycling and exercise-induced neuromuscular alterations.

METHODS

Twenty-two participants completed three sessions 2-3 wk apart: 1) determination of the maximal concentric peak power output, familiarization with eccentric cycling at two cadences (30 and 60 rpm at 60% peak power output), and neuromuscular testing procedure; 2) and 3) 30 min of eccentric cycling exercise at a cadence of 30 or 60 rpm. PE, cardiorespiratory parameters, and vastus lateralis and rectus femoris EMG were collected during exercise. The knee extensors' maximal voluntary contraction torque, the torque evoked by double stimulations at 100 Hz (Dt100) and 10 Hz (Dt10), and the voluntary activation level were evaluated before and after exercise.

RESULTS

V̇O 2 , EMG, and PE were greater at 30 than 60 rpm (all P < 0.05). Maximal voluntary contraction torque, evoked torque, and Dt10/Dt100 ratio decreased (all P < 0.01) without cadence effect (all P > 0.28). Voluntary activation level remained constant after both eccentric cycling exercises ( P = 0.87).

CONCLUSIONS

When performed at the same power output, eccentric cycling exercise at 30 rpm elicited a greater PE, EMG, and cardiorespiratory demands than pedaling at 60 rpm. Exercise-induced fatigability was similar in both eccentric cycling conditions without neural impairments, suggesting that eccentric cycling seemed to alter more specifically muscular function, such as the excitation-contraction coupling process. In a rehabilitation context, eccentric cycling at 60 rpm seems more appropriate because it will induce lower PE for similar strength loss compared with 30 rpm.

More Variability in Tibialis Anterior Function during the Adduction of the Foot than Dorsiflexion of the Ankle

INTRODUCTION

The aim of the study was to compare maximal force, force steadiness, and the discharge characteristics of motor units in the tibialis anterior (TA) muscle during submaximal isometric contractions for ankle dorsiflexion and adduction of the foot.

METHODS

Nineteen active young adults performed maximal and submaximal isometric dorsiflexion and adduction contractions at five target forces (5%, 10%, 20%, 40%, and 60% maximal voluntary contraction [MVC]). The activity of motor units in TA was recorded by high-density EMG.

RESULTS

The maximal force was similar between dorsiflexion and adduction, despite EMG amplitude for TA being greater ( P < 0.05) during dorsiflexion than adduction. Τhe coefficient of variation (CV) for force (force steadiness) during dorsiflexion was always less ( P < 0.05) than during adduction, except of 5% MVC force. No differences were observed for mean discharge rate; however, the regression between the changes in discharge rate relative to the change of force was significant for dorsiflexion ( R2 = 0.25, P < 0.05) but not for adduction. Discharge variability, however, was usually less during dorsiflexion. The CV for interspike interval was less ( P < 0.05) at 10%, 20%, and 40% MVC but greater at 60% MVC during dorsiflexion than adduction. Similarly, the SD values of the filtered cumulative spike train of the motor units in TA were less ( P < 0.05) at 5%, 10%, 20%, and 40% MVC during dorsiflexion than adduction.

CONCLUSIONS

Although the mean discharge rate of motor units in TA was similar during foot adduction and ankle dorsiflexion, discharge variability was less during dorsiflexion resulting in less accurate performance of the steady adduction contractions. The neural drive to bifunctional muscles differs during their accessory function, which must be considered for training and rehabilitation interventions.

A systematic investigation of detectors for low signal-to-noise ratio EMG signals

Background

Active participation of stroke survivors during robot-assisted movement therapy is essential for sensorimotor recovery. Robot-assisted therapy contingent on movement intention is an effective way to encourage patients' active engagement. For severely impaired stroke patients with no residual movements, a surface electromyogram (EMG) has been shown to be a viable option for detecting movement intention. Although numerous algorithms for EMG detection exist, the detector with the highest accuracy and lowest latency for low signal-to-noise ratio (SNR) remains unknown.

Methods

This study, therefore, investigates the performance of 13 existing EMG detection algorithms on simulated low SNR (0dB and -3dB) EMG signals generated using three different EMG signal models: Gaussian, Laplacian, and biophysical model. The detector performance was quantified using the false positive rate (FPR), false negative rate (FNR), and detection latency. Any detector that consistently showed FPR and FNR of no more than 20%, and latency of no more than 50ms, was considered an appropriate detector for use in robot-assisted therapy.

Results

The results indicate that the Modified Hodges detector - a simplified version of the threshold-based Hodges detector introduced in the current study - was the most consistent detector across the different signal models and SNRs. It consistently performed for ~90% and ~40% of the tested trials for 0dB and -3dB SNR, respectively. The two statistical detectors (Gaussian and Laplacian Approximate Generalized Likelihood Ratio) and the Fuzzy Entropy detectors have a slightly lower performance than Modified Hodges.

Conclusions

Overall, the Modified Hodges, Gaussian and Laplacian Approximate Generalized Likelihood Ratio, and the Fuzzy Entropy detectors were identified as the potential candidates that warrant further investigation with real surface EMG data since they had consistent detection performance on low SNR EMG data.

Experimental evaluation of the impact of sEMG interfaces in enhancing embodiment of virtual myoelectric prostheses

INTRODUCTION

Despite recent technological advances that have led to sophisticated bionic prostheses, attaining embodied solutions still remains a challenge. Recently, the investigation of prosthetic embodiment has become a topic of interest in the research community, which deals with enhancing the perception of artificial limbs as part of users' own body. Surface electromyography (sEMG) interfaces have emerged as a promising technology for enhancing upper-limb prosthetic control. However, little is known about the impact of these sEMG interfaces on users' experience regarding embodiment and their interaction with different functional levels.

METHODS

To investigate this aspect, a comparison is conducted among sEMG configurations with different number of sensors (4 and 16 channels) and different time delay. We used a regression algorithm to simultaneously control hand closing/opening and forearm pronation/supination in an immersive virtual reality environment. The experimental evaluation includes 24 able-bodied subjects and one prosthesis user. We assess functionality with the Target Achievement Control test, and the sense of embodiment with a metric for the users perception of self-location, together with a standard survey.

RESULTS

Among the four tested conditions, results proved a higher subjective embodiment when participants used sEMG interfaces employing an increased number of sensors. Regarding functionality, significant improvement over time is observed in the same conditions, independently of the time delay implemented.

CONCLUSIONS

Our work indicates that a sufficient number of sEMG sensors improves both, functional and subjective embodiment outcomes. This prompts discussion regarding the potential relationship between these two aspects present in bionic integration. Similar embodiment outcomes are observed in the prosthesis user, showing also differences due to the time delay, and demonstrating the influence of sEMG interfaces on the sense of agency.

Decoding of unimanual and bimanual reach-and-grasp actions from EMG and IMU signals in persons with cervical spinal cord injury

Objective. Chronic motor impairments of arms and hands as the consequence of a cervical spinal cord injury (SCI) have a tremendous impact on activities of daily life. A considerable number of people however retain minimal voluntary motor control in the paralyzed parts of the upper limbs that are measurable by electromyography (EMG) and inertial measurement units (IMUs). An integration into human-machine interfaces (HMIs) holds promise for reliable grasp intent detection and intuitive assistive device control.Approach. We used a multimodal HMI incorporating EMG and IMU data to decode reach-and-grasp movements of groups of persons with cervical SCI (n = 4) and without (control, n = 13). A post-hoc evaluation of control group data aimed to identify optimal parameters for online, co-adaptive closed-loop HMI sessions with persons with cervical SCI. We compared the performance of real-time, Random Forest-based movement versus rest (2 classes) and grasp type predictors (3 classes) with respect to their co-adaptation and evaluated the underlying feature importance maps.Main results. Our multimodal approach enabled grasp decoding significantly better than EMG or IMU data alone (p<0.05). We found the 0.25 s directly prior to the first touch of an object to hold the most discriminative information. Our HMIs correctly predicted 79.3 ± STD 7.4 (102.7 ± STD 2.3 control group) out of 105 trials with grand average movement vs. rest prediction accuracies above 99.64% (100% sensitivity) and grasp prediction accuracies of 75.39 ± STD 13.77% (97.66 ± STD 5.48% control group). Co-adaption led to higher prediction accuracies with time, and we could identify adaptions in feature importances unique to each participant with cervical SCI.Significance. Our findings foster the development of multimodal and adaptive HMIs to allow persons with cervical SCI the intuitive control of assistive devices to improve personal independence.

Arm muscle synergies enhance hand posture prediction in combination with forearm muscle synergies

Objective.We analyze and interpret arm and forearm muscle activity in relation with the kinematics of hand pre-shaping during reaching and grasping from the perspective of human synergistic motor control.Approach.Ten subjects performed six tasks involving reaching, grasping and object manipulation. We recorded electromyographic (EMG) signals from arm and forearm muscles with a mix of bipolar electrodes and high-density grids of electrodes. Motion capture was concurrently recorded to estimate hand kinematics. Muscle synergies were extracted separately for arm and forearm muscles, and postural synergies were extracted from hand joint angles. We assessed whether activation coefficients of postural synergies positively correlate with and can be regressed from activation coefficients of muscle synergies. Each type of synergies was clustered across subjects.Main results.We found consistency of the identified synergies across subjects, and we functionally evaluated synergy clusters computed across subjects to identify synergies representative of all subjects. We found a positive correlation between pairs of activation coefficients of muscle and postural synergies with important functional implications. We demonstrated a significant positive contribution in the combination between arm and forearm muscle synergies in estimating hand postural synergies with respect to estimation based on muscle synergies of only one body segment, either arm or forearm (p< 0.01). We found that dimensionality reduction of multi-muscle EMG root mean square (RMS) signals did not significantly affect hand posture estimation, as demonstrated by comparable results with regression of hand angles from EMG RMS signals.Significance.We demonstrated that hand posture prediction improves by combining activity of arm and forearm muscles and we evaluate, for the first time, correlation and regression between activation coefficients of arm muscle and hand postural synergies. Our findings can be beneficial for myoelectric control of hand prosthesis and upper-limb exoskeletons, and for biomarker evaluation during neurorehabilitation.

Effects of prolonged vibration to the flexor carpi radialis muscle on intracortical excitability

Prolonged local vibration (LV) can induce neurophysiological adaptations thought to be related to long-term potentiation or depression. Yet, how changes in intracortical excitability may be involved remains to be further investigated as previous studies reported equivocal results. We therefore investigated the effects of 30 min of LV applied to the right flexor carpi radialis muscle (FCR) on both short-interval intracortical inhibition (SICI) and intracortical facilitation (ICF). SICI and ICF were measured through transcranial magnetic stimulation before and immediately after 30 min of FCR LV (vibration condition) or 30 min of rest (control condition). Measurements were performed during a low-intensity contraction (n = 17) or at rest (n = 7). No significant SICI nor ICF modulations were observed, whether measured during isometric contractions or at rest (p = 0.2). Yet, we observed an increase in inter-individual variability for post measurements after LV. In conclusion, while intracortical excitability was not significantly modulated after LV, increased inter-variability observed after LV may suggest the possibility of divergent responses to prolonged LV exposure.

Vocal fold electromyography in patients with endoscopic features of unilateral laryngeal paralysis

<b><br>Introduction:</b> Electromyography (EMG) of the larynx provides information on the electrophysiological condition of laryngeal muscles and innervation. Integration of information obtained from the EMG exams with the clinical parameters as obtained by other methods for laryngeal assessment (endoscopy, perceptual and acoustic analysis, voice self-assessment) provides a multidimensional picture of dysphonia, which is of particular importance in patients with vocal fold (VF) mobility disorders accompanied by glottic insufficiency.</br> <b><br>Aim:</b> The aim of this study was to evaluate laryngeal EMG records acquired in subjects with unilateral vocal fold immobilization with signs of atrophy and glottic insufficiency.</br> <b><br>Material and methods:</b> From the available material of 74 EMG records of patients referred for the exam due to unilateral laryngeal paralysis, records of 17 patients with endoscopic features suggestive of complete laryngeal muscle denervation were selected. The EMG study of thyroarytenoid muscles of mobile and immobile VFs was evaluated qualitatively and quantitatively at rest and during volitional activity involving free phonation of vowel /e/ [ε].</br> <b><br>Results:</b> In all patients, the EMG records from mobile VFs were significantly different from those from immobile VFs. Despite endoscopic features of paralysis, no VF activity whatsoever was observed in as few as 2 patients so as to meet the neurophysiological definition of paralysis. In 88% of cases, electromyographic activity of the thyroarytenoid muscle was observed despite immobilization and atrophy of the vocal fold. In these patients, neurogenic type of record was observed with numerous high- -amplitude mobility units. On the basis of the results, quantitative features of EMG records indicative of paralysis and residual activity of the thyroarytenoid muscle were determined.</br> <b><br>Conclusions:</b> Qualitative and quantitative analysis of laryngeal EMG records provides detailed information on the condition of vocal fold muscles and innervation. EMG records of mobile vs immobile VFs differ significantly from each other. Endoscopic evaluation does not provide sufficient basis for the diagnosis of complete laryngeal muscle denervation.</br>.

Does Exerting Grasps Involve a Finite Set of Muscle Patterns? A Study of Intra- and Intersubject Variability of Forearm sEMG Signals in Seven Grasp Types

Surface Electromyography (sEMG) signals are widely used as input to control robotic devices, prosthetic limbs, exoskeletons, among other devices, and provide information about someone's intention to perform a particular movement. However, the redundant action of 32 muscles in the forearm and hand means that the neuromotor system can select different combinations of muscular activities to perform the same grasp, and these combinations could differ among subjects, and even among the trials done by the same subject. In this work, 22 healthy subjects performed seven representative grasp types (the most commonly used). sEMG signals were recorded from seven representative forearm spots identified in a previous work. Intra- and intersubject variability are presented by using four sEMG characteristics: muscle activity, zero crossing, enhanced wavelength and enhanced mean absolute value. The results confirmed the presence of both intra- and intersubject variability, which evidences the existence of distinct, yet limited, muscle patterns while executing the same grasp. This work underscores the importance of utilizing diverse combinations of sEMG features or characteristics of various natures, such as time-domain or frequency-domain, and it is the first work to observe the effect of considering different muscular patterns during grasps execution. This approach is applicable for fine-tuning the control settings of current sEMG devices.

M2M-InvNet: Human Motor Cortex Mapping From Multi-Muscle Response Using TMS and Generative 3D Convolutional Network

Transcranial magnetic stimulation (TMS) is often applied to the motor cortex to stimulate a collection of motor evoked potentials (MEPs) in groups of peripheral muscles. The causal interface between TMS and MEP is the selective activation of neurons in the motor cortex; moving around the TMS 'spot' over the motor cortex causes different MEP responses. A question of interest is whether a collection of MEP responses can be used to identify the stimulated locations on the cortex, which could potentially be used to then place the TMS coil to produce chosen sets of MEPs. In this work we leverage our previous report on a 3D convolutional neural network (CNN) architecture that predicted MEPs from the induced electric field, to tackle an inverse imaging task in which we start with the MEPs and estimate the stimulated regions on the motor cortex. We present and evaluate five different inverse imaging CNN architectures, both conventional and generative, in terms of several measures of reconstruction accuracy. We found that one architecture, which we propose as M2M-InvNet, consistently achieved the best performance.

Online prediction of sustained muscle force from individual motor unit activities using adaptive surface EMG decomposition

Decoding movement intentions from motor unit (MU) activities to represent neural drive information plays a central role in establishing neural interfaces, but there remains a great challenge for obtaining precise MU activities during sustained muscle contractions. In this paper, we presented an online muscle force prediction method driven by individual MU activities that were decomposed from prolonged surface electromyogram (SEMG) signals in real time. In the training stage of the proposed method, a set of separation vectors was initialized for decomposing MU activities. After transferring each decomposed MU activity into a twitch force train according to its action potential waveform, a neural network was designed and trained for predicting muscle force. In the subsequent online stage, a practical double-thread-parallel algorithm was developed. One frontend thread predicted the muscle force in real time utilizing the trained network and the other backend thread simultaneously updated the separation vectors. To assess the performance of the proposed method, SEMG signals were recorded from the abductor pollicis brevis muscles of eight subjects and the contraction force was simultaneously collected. With the update procedure in the backend thread, the force prediction performance of the proposed method was significantly improved in terms of lower root mean square deviation (RMSD) of around 10% and higher fitness (R2) of around 0.90, outperforming two conventional methods. This study provides a promising technique for real-time myoelectric applications in movement control and health.

Adaptive Online Decomposition of Surface EMG Using Progressive FastICA Peel-Off

This study presents a method for adaptive online decomposition of high-density surface electromyogram (SEMG) signals to overcome the performance degradation during long-term recordings. The proposed method utilized the progressive FastICA peel-off (PFP) method and integrated a practical double-thread-parallel algorithm into the conventional two-stage calculation approach. During the offline initialization stage, a set of separation vectors was computed. In the subsequent online decomposition stage, a backend thread was implemented to periodically update the separation vectors using the constrained FastICA algorithm and the automatic PFP method. Concurrently, the frontend thread employed the newly updated separation vectors to accurately extract motor unit (MU) spike trains in real time. To assess the effectiveness of the proposed method, simulated and experimental SEMG signals from abductor pollicis brevis muscles of ten subjects were used for evaluation. The results demonstrated that the proposed method outperformed the conventional method, which relies on fixed separation vectors. Specifically, the proposed method showed an improved matching rate by 3.63% in simulated data and 1.98% in experimental data, along with an increased motor unit number by 2.39 in simulated data and 1.30 in experimental data. These findings illustrated the feasibility of the proposed method to enhance the performance of online SEMG decomposition. As a result, this work holds promise for various applications that require accurate MU firing activities in decoding neural commands and building neural-machine interfaces.

Effectiveness of the sternocleidomastoid muscle contraction asymmetry and filter: cervical vestibular evoked myogenic potential

OBJECTIVE

This study aimed to determine the precautions that can be taken to increase the reliability of the vestibular evoked myogenic potentials test without being affected by the asymmetry of the sternocleidomastoid muscle and the issues that should be considered in the interpretation of vestibular evoked myogenic potential results if these precautions are not taken.

METHOD

Individuals with sternocleidomastoid muscle activity of less than 30 μV in cervical vestibular evoked myogenic potential testing and an asymmetry ratio of more than 0.35 were excluded. In our study, individuals were divided into different groups according to sternocleidomastoid muscle asymetry.

RESULTS

A total of 53 individuals were included in the study. Intergroup comparisons were made to determine the effect of electromyogram scaling and filter use on amplitude asymmetry ratio according to sternocleidomastoid muscle asymmetry.

CONCLUSION

Keeping the sternocleidomastoid muscle asymmetry not exceeding 10 μV maximises the reliability of cervical vestibular evoked myogenic potentials. As a result of our study, it can be concluded that in clinical applications the asymmetry should not exceed 20 μV.

Antagonism of 5-HT2 receptors attenuates self-sustained firing of human motor units

5-HT2 receptors on motoneurones play a critical role in facilitating persistent inward currents (PICs). Although facilitation of PICs can enhance self-sustained firing after periods of excitation, the relationship between 5-HT2 receptor activity and self-sustained firing in human motor units (MUs) has not been resolved. MU activity was assessed from the tibialis anterior of 10 healthy adults (24.9 ± 2.8 years) during two contraction protocols. Both protocols featured steady-state isometric contractions with constant descending drive to the motoneurone pool. However, one protocol also included an additional phase of superimposed descending drive. Adding and then removing descending drive in the middle of steady-state contractions altered MU firing behaviour across the motor pool, where newly recruited units in the superimposed phase were unable to switch off (P = 0.0002), and units recruited prior to additional descending drive reduced their discharge rates (P < 0.0001, difference in estimated marginal means (∆) = 2.24 pulses/s). The 5-HT2 receptor antagonist, cyproheptadine, was then administered to determine whether changes in MU firing were mediated by serotonergic mechanisms. 5-HT2 receptor antagonism caused reductions in MU discharge rate (P < 0.001, ∆ = 1.65 pulses/s), recruitment threshold (P = 0.00112, ∆ = 1.09% maximal voluntary contraction) and self-sustained firing duration (P < 0.0001, ∆ = 1.77s) after the additional descending drive was removed in the middle of the steady-state contraction. These findings indicate that serotonergic neuromodulation plays a key role in facilitating discharge and self-sustained firing of human motoneurones, where adaptive changes in MU recruitment must occur to meet the demands of the contraction. KEY POINTS: Animal and cellular preparations indicate that somato-dendritic 5-HT2 receptors regulate the intrinsic excitability of motoneurones. 5-HT2 receptor antagonism reduces estimates of persistent inward currents in motoneurones, which contribute to self-sustained firing when synaptic inputs are reduced or removed. This human study employed a contraction task that slowly increased (and then removed) the additional descending drive in the middle of a steady-state contraction where marked self-sustained firing occurred when the descending drive was removed. 5-HT2 receptor antagonism caused widespread reductions in motor unit (MU) discharge rates during contractions, which was accompanied by reduced recruitment threshold and attenuation of self-sustained firing duration after the removal of the additional descending drive to motoneurones. These findings support the role that serotonergic neuromodulation is a key facilitator of MU discharge and self-sustained firing of human motoneurones, where adaptative changes in MU recruitment must occur to meet the demands of the contraction.

Excitation distribution of the trapezius changes in response to increasing contraction intensity, but not repeated contractions

Upper trapezius (UT) excitation redistributes with experimentally-induced muscle pain, fatigue, and repeated contractions. Excitation distribution variability is proposed to reduce the likelihood of shoulder pain and pathology by reducing cumulative stress on musculoskeletal structures. While the middle (MT) and lower (LT) trapezius are pivotal in scapular stabilization, it remains unclear whether they display similar excitation distribution variability with repeated or increasing contraction intensity. We determined if excitation distribution of the UT, MT, and LT differ: 1) during isometric contractions at different intensities (30 % and 60 % of maximum voluntary isometric contraction (MVIC)); and 2) with repeated contractions at 60 % MVIC. Nineteen individuals completed MVICs and submaximal contractions for the UT, MT, and LT while high-density electromyography was collected. Statistical parametric mapping t-tests were performed between intensities and the 1st and 5th repetition at 60 % MVIC. UT, MT, and LT excitation distribution changed with increasing contraction intensity in 358 (∼92 % of the map), 54 (∼14 %), and 270 pixels (∼70 %), respectively. No pixels exceeded significance with repeated contractions for any muscle. Barycentre analyses revealed no significant results. These results suggest that regions of the trapezius muscle use different neuromuscular strategies in response to changes in contraction intensity and repeated contractions.

Muscle fatigue tracking during dynamic elbow flexion-extension movements with a varying hand load

Muscle fatigue monitoring, an important element in a fatigue risk management process, can help optimize work intensity and reduce risks for musculoskeletal injuries. An experiment was conducted to determine whether myoelectric manifestations of muscle fatigue can reflect the pace of fatigue development associated with varying load intensity. Twenty male participants performed elbow flexion-extension movements with alternating hand loads (2 kg vs. 1 kg) for 16 min. The pace of fatigue in the biceps brachii in response to load variation was quantified by electromyographic (EMG) fatigue measures collected during the dynamic elbow flexion-extension movements and periodic submaximal isometric elbow flexion trials. The isometric and dynamic EMG measures, except for the amplitude of dynamic EMG, indicated fatigue development during the 2-kg isotonic movements and partial recovery with the 1 kg load. Study results suggest the potential of EMG measures for fatigue monitoring during dynamic work tasks with varying load intensity.

Motor unit modes in the calf muscles during a submaximal isometric contraction are changed by brief stretches

The purpose of our study was to investigate the influence of a stretch intervention on the common modulation of discharge rate among motor units in the calf muscles during a submaximal isometric contraction. The current report comprises a computational analysis of a motor unit dataset that we published previously (Mazzo et al., 2021). Motor unit activity was recorded from the three main plantar flexor muscles while participants performed an isometric contraction at 10% of the maximal voluntary contraction force before and after each of two interventions. The interventions were a control task (standing balance) and static stretching of the plantar flexor muscles. A factorization analysis on the smoothed discharge rates of the motor units from all three muscles yielded three modes that were independent of the individual muscles. The composition of the modes was not changed by the standing-balance task, whereas the stretching exercise reduced the average correlation in the second mode and increased it in the third mode. A centroid analysis on the correlation values showed that most motor units were associated with two or three modes, which were presumed to indicate shared synaptic inputs. The percentage of motor units adjacent to the seven centroids changed after both interventions: Control intervention, mode 1 decreased and the shared mode 1 + 2 increased; stretch intervention, shared modes either decreased (1 + 2) or increased (1 + 3). These findings indicate that the neuromuscular adjustments during both interventions were sufficient to change the motor unit modes when the same task was performed after each intervention. KEY POINTS: Based on covariation of the discharge rates of motor units in the calf muscles during a submaximal isometric contraction, factor analysis was used to assign the correlated discharge trains to three motor unit modes. The motor unit modes were determined from the combined set of all identified motor units across the three muscles before and after each participant performed a control and a stretch intervention. The composition of the motor unit modes changed after the stretching exercise, but not after the control task (standing balance). A centroid analysis on the distribution of correlation values found that most motor units were associated with a shared centroid and this distribution, presumably reflecting shared synaptic input, changed after both interventions. Our results demonstrate how the distribution of multiple common synaptic inputs to the motor neurons innervating the plantar flexor muscles changes after a brief series of stretches.

Estimation of electrical muscle activity during gait using inertial measurement units with convolution attention neural network and small-scale dataset

In general, muscle activity can be directly measured using Electromyography (EMG) or calculated with musculoskeletal models. However, both methods are not suitable for non-technical users and unstructured environments. It is desired to establish more portable and easy-to-use muscle activity estimation methods. Deep learning (DL) models combined with inertial measurement units (IMUs) have shown great potential to estimate muscle activity. However, it frequently occurs in clinical scenarios that a very small amount of data is available and leads to limited performance of the DL models, while the augmentation techniques to efficiently expand a small sample size for DL model training are rarely used. The primary aim of the present study was to develop a novel DL model to estimate the EMG envelope during gait using IMUs with high accuracy. A secondary aim was to develop a novel model-based data augmentation method to improve the performance of the estimation model with small-scale dataset. Therefore, in the present study, a time convolutional network-based generative adversarial network, namely MuscleGAN, was proposed for data augmentation. Moreover, a subject-independent regression DL model was developed to estimate EMG envelope. Results suggested that the proposed two-stage method has better generalization and estimation performance than the commonly used existing methods. Pearson correlation coefficient and normalized root-mean-square errors derived from the proposed method reached up to 0.72 and 0.13, respectively. It was indicated that the MuscleGAN indeed improved the estimation accuracy of lower limb EMG envelope from 70% to 72%. Thus, even using only two IMUs and a very small-scale dataset, the proposed model is still capable of accurately estimating lower limb EMG envelope, demonstrating considerable potential for its application in clinical and daily life scenarios.

Investigation of motor unit behavior in exercise and sports physiology: challenges and perspectives

Several methods are in use to record and analyze neuronal activation, each with specific advantages and challenges. New developments like the decomposition of high-density surface electromyography (HDsEMG) have enabled novel insights into discharge characteristics noninvasively in laboratory settings but face certain challenges to be applied in sports physiology in a broader scope. Several challenges can be accounted for by methodological considerations, others require further technological developments to allow this technology to be used in more applied settings. This paper aims to describe the developments of surface electromyography and identify the challenges and perspectives of HDsEMG in the context of an application in sports physiology. We further discuss methodological possibilities to overcome some of the challenges to investigate specific research questions and identify areas that require further advancements.

AI-Enabled Soft Sensing Array for Simultaneous Detection of Muscle Deformation and Mechanomyography for Metaverse Somatosensory Interaction

Motion recognition (MR)-based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human-computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non-intrusive muscle-sensing wearable device, that in conjunction with machine learning, enables motion-control-based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16-channel pressure sensor array (weighing ≈0.38 g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower-limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle-sensing-based somatosensory interaction, using the proposed wearable device, for enabling the real-time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography-based methods for achieving accurate human motion capture, which can further broaden the applications of motion-interactive wearable devices for the coming metaverse age.

Comparison of neuromuscular fatigability amplitude and etiologies between fatigued and non-fatigued cancer patients

PURPOSE

Cancer-related fatigue (CRF) is the most reported side effect of cancer and its treatments. Mechanisms of CRF are multidimensional, including neuromuscular alterations leading to decreased muscle strength and endurance (i.e., fatigability). Recently, exercise fatigability and CRF have been related, while fatigability mechanisms remain unclear. Traditionally, fatigability is assessed from maximal voluntary contractions (MVC) decrease, but some authors hypothesized that the rate of force development (RFD) determined during a rapid contraction could also be an interesting indicator of functional alterations. However, to our knowledge, no study investigated RFD in cancer patients. The purpose of this study was to determine whether RFD, fatigability amplitude, and etiology are different between fatigued and non-fatigued cancer patients.

METHODS

Eighteen participants with cancer, divided in fatigued or non-fatigued groups according their CRF level, completed a 5-min all-out exercise in ankle plantar flexor muscles composed of 62 isometric MVC of 4 s with 1 s rest, to assess fatigability amplitude as the force-time relationship asymptote (FA). Before and after exercise, fatigability etiologies (i.e., voluntary activation (VA) and evoked forces by electrical stimulation (Db100)) were assessed as well as RFD in 50 and 100 ms (RFD50 and RFD100, respectively) during rapid contractions.

RESULTS

FA is significantly lower in fatigued group. Significant differences were found between pre- and post-exercise VA, Db100, RFD50, and RFD100 for both groups, with no statistical difference between groups.

CONCLUSION

During treatments, fatigability is higher in fatigued patients; however, the mechanisms of fatigability and RFD alterations are similar in both groups.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT04391543, May 2020.

Monitoring Changes in TMS-Evoked EEG and EMG Activity During 1 Hz rTMS of the Healthy Motor Cortex

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive brain stimulation technique capable of inducing neuroplasticity as measured by changes in peripheral muscle electromyography (EMG) or electroencephalography (EEG) from pre-to-post stimulation. However, temporal courses of neuromodulation during ongoing rTMS are unclear. Monitoring cortical dynamics via TMS-evoked responses using EMG (motor-evoked potentials; MEPs) and EEG (transcranial-evoked potentials; TEPs) during rTMS might provide further essential insights into its mode of action - temporal course of potential modulations. The objective of this study was to first evaluate the validity of online rTMS-EEG and rTMS-EMG analyses, and second to scrutinize the temporal changes of TEPs and MEPs during rTMS. As rTMS is subject to high inter-individual effect variability, we aimed for single-subject analyses of EEG changes during rTMS. Ten healthy human participants were stimulated with 1,000 pulses of 1 Hz rTMS over the motor cortex, while EEG and EMG were recorded continuously. Validity of MEPs and TEPs measured during rTMS was assessed in sensor and source space. Electrophysiological changes during rTMS were evaluated with model fitting approaches on a group- and single-subject level. TEPs and MEPs appearance during rTMS was consistent with past findings of single pulse experiments. Heterogeneous temporal progressions, fluctuations or saturation effects of brain activity were observed during rTMS depending on the TEP component. Overall, global brain activity increased over the course of stimulation. Single-subject analysis revealed inter-individual temporal courses of global brain activity. The present findings are in favor of dose-response considerations and attempts in personalization of rTMS protocols.

Instrumental assessment of sleep bruxism: A systematic review and meta-analysis

This systematic review and meta-analysis (MA) aimed to evaluate the diagnostic validity of portable electromyography (EMG) diagnostic devices compared to the reference standard method polysomnography (PSG) in assessing sleep bruxism. This systematic review was completed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement and was registered with PROSPERO prior to the accomplishment of the main search. Ten clinical studies on humans, assessing the diagnostic accuracy of portable instrumental approaches with respect to PSG, were included in the review. Methodological shortcomings were identified by QUADAS-2 quality assessment. The certainty of the evidence analysis was established by different levels of evidence according to the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) framework. A meta-analysis of diagnostic test accuracy was performed with multiple thresholds per study applying a two-stage random effects model, using the thresholds offered by the studies and based on the number of EMG bruxism events per hour presented by the participants. Five studies were included. The MA indicated that portable EMG diagnostic devices showed a very good diagnostic capacity, although a high variability is evident in the studies with some outliers. Very low quality of evidence due to high risk of bias and high heterogeneity among included studies suggests that portable devices have shown high sensitivity and specificity when diagnosing sleep bruxism (SB) compared to polysomnography. The tests performed in the MA found an estimated optimal cut-off point of 7 events/hour of SB with acceptably high sensitivity and specificity for the EMG portable devices.

Effect of combined electrical stimulation and brief muscle lengthening on torque development

This study aimed to evaluate torque production in response to the application of a brief muscle lengthening during neuromuscular electrical stimulation (NMES) applied over the posterior tibial nerve. Fifteen participants took part in three experimental sessions, where wide-pulse NMES delivered at 20 and 100 Hz (pulse duration of 1 ms applied during 15 s at an intensity evoking 5-10% of maximal voluntary contraction) was either applied alone (NMES condition) or in combination with a muscle lengthening at three distinct speeds (60, 180, or 300°/s; NMES + LEN condition). The torque-time integral (TTI) and the muscle activity following the stimulation trains [sustained electromyography (EMG)] were calculated for each condition. Results show that TTI and sustained EMG activity were higher for the NMES + LEN condition only when using 100-Hz stimulation, regardless of the lengthening speed (P = 0.029 and P = 0.007 for the two parameters, respectively). This indicates that superimposing a muscle lengthening to high-frequency NMES can enhance the total torque production, partly due to neural mechanisms, as evidenced by the higher sustained EMG activity. This finding has potential clinical relevance, especially when it comes to finding ways to enhance torque production to optimize the effectiveness of NMES training programs.NEW & NOTEWORTHY This study showed, for the first time, that the combined application of a brief muscle lengthening and wide-pulse neuromuscular electrical stimulation (NMES) delivered over the posterior tibial nerve can entail increased torque production as compared with the sole application of NMES. This observation, present only for high stimulation frequencies (100 Hz) and independently of the lengthening speed, is attributed to neural mechanisms, most probably related to increased afferents' solicitation, although muscular phenomena cannot be excluded.

Graded onset of parasternal intercostal inspiratory activity detected with surface electromyography in healthy young females and males

Intramuscular recordings of single motor unit activity from parasternal intercostal muscles show a rostrocaudal gradient in timing and amplitude of inspiratory activity. This study determined the feasibility of surface electromyographic activity (EMG) to measure graded parasternal intercostal activity in young females and males during quiet breathing and breathing with inspiratory resistive loads. Surface EMGs were recorded from the 1st-to-5th parasternal intercostal muscles during 10 min of breathing. EMGs were processed to remove 50 Hz and electrocardiogram artifacts and integrated. Amplitude and onset time of inspiratory activity were measured from waveform averages triggered at the onset of inspiratory flow. Onset times were measured independently by two assessors, blinded to interspace and EMG scale, with excellent agreement (ICC3,k = 0.86). The onset of inspiratory activity in the 1st-to-3rd interspaces was at or within ∼400 ms of the start of inspiratory airflow, but activity in the caudal (4th and 5th) spaces was delayed by up to ∼1,000 ms (P < 0.001). There was no main effect of sex on onset time (P = 0.07), but an interaction with interspace (P < 0.001) revealed that inspiratory activity in the caudal interspaces was delayed by 15% of inspiratory time in female participants compared with 30% of inspiratory time in male participants. Inspiratory loads did not affect EMG onset time (P = 0.31). Thus, surface EMG is feasible to assess the onset time of inspiratory activity as a marker of inspiratory neural drive and pattern of activation across spaces, in both females and males.NEW & NOTEWORTHY We demonstrated that surface EMG is a valid method to measure graded inspiratory EMG in the parasternal intercostal muscles in healthy young male and female participants during quiet breathing and loaded breathing. Across the 1st-to-5th interspaces, there was more homogenous activation in women and more graded activity in men across parasternal intercostal muscles during breathing. By recording surface EMG from both male and female participants, we have revealed sex differences in inspiratory activity across intercostal muscles.

Hemodynamic changes in the temporalis and masseter muscles during acute stress in healthy humans

PURPOSE

Autonomic control of orofacial areas is an integral part of the stress response, controlling functions such as pupil dilatation, salivation, and skin blood flow. However, the specific control of blood flow in head muscles during stress is unknown. This study aims to investigate the hemodynamic response of temporalis and masseter muscles in response to five different stressors.

METHODS

Sixteen healthy individuals were subjected to a randomized series of stressors, including cold pressor test, mental arithmetic test, apnea, isometric handgrip, and post-handgrip muscle ischemia, while in the sitting posture. Finger-pulse photoplethysmography was used to measure arterial blood pressure, heart rate, and cardiac output. Near-infrared spectroscopy was used to measure changes in tissue oxygenation and hemoglobin indices from the temporalis and masseter muscles.

RESULTS

All stressors effectively and significantly increased arterial blood pressure. Tissue oxygenation index significantly increased in both investigated head muscles during mental arithmetic test (temporalis: 4.22 ± 3.52%; masseter: 3.43 ± 3.63%) and isometric handgrip (temporalis: 3.45 ± 3.09%; masseter: 3.26 ± 3.07%), suggesting increased muscle blood flow. Neither the masseter nor the temporalis muscles evidenced a vasoconstrictive response to any of the stressors tested.

CONCLUSION

In the different conditions, temporalis and masseter muscles exhibited similar hemodynamic patterns of response, which do not include the marked vasoconstriction generally observed in limb muscles. The peculiar sympathetic control of head muscles is possibly related to the involvement of these muscles in aggressive/defensive reactions and/or to their unfavorable position with regard to hydrostatic blood levels.

[Validation of the flexion-relaxation test to define a lumbar deficit with tetrapolar electrodes]

INTRODUCTION AND OBJECTIVE

To obtain a new cut-off point (CP) for a lumbar flexion-relaxation (RF) test established with tetrapolar (e.) electrodes, from values already defined with bipolar devices.

MATERIALS AND METHODS

The study sample consists of 47 patients in a situation of temporary disability due to low back pain (DL). They were evaluated by means of an isometric dynamometry test, a kinematic test and an assessment of the FR phenomenon. Two experiments with ROC curves are proposed. The first, with 47 patients who consecutively performed the RF test with both types of electrodes, using the cut-off point (CP) known for the e. bipolar (2.49μV). In the second, with the EMG data recorded with e. tetrapolar in 17 patients, a DeLong test was performed that compares the 2 ROC curves that were constructed on the one hand, by classifying the sample from dynamometry and kinematic tests, and on the other, by classifying them with the bipolar EMG values.

RESULTS

A total of 34 patients adequately completed the evaluations of the first experiment and 17 patients the second. The first study shows a cut-off point of 1.2μV, with an AUC of 87.7%; Sensitivity 84.2% and Specificity 80%. The second shows a PC for e. bipolars of 1.21μV (AUC 87.5%) and for e. tetrapolar values of 1.43 (AUC 82.5%) with a DeLong test without significant differences between both curves (p>0.4065).

CONCLUSIONS

The validation methodology with ROC curves has made it possible to obtain a new PC for the RF test in a practical way, simply by simultaneously performing both tests on the same group of patients until a significant sample is obtained.

Influence of spatio-temporal filtering on hand kinematics estimation from high-density EMG signals. J Neural Eng 2024;21:026014. [PMID: 38525843 DOI: 10.1088/1741-2552/ad3498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Objective.Surface electromyography (sEMG) is a non-invasive technique that records the electrical signals generated by muscles through electrodes placed on the skin. sEMG is the state-of-the-art method used to control active upper limb prostheses because of the association between its amplitude and the neural drive sent from the spinal cord to muscles. However, accurately estimating the kinematics of a freely moving human hand using sEMG from extrinsic hand muscles remains a challenge. Deep learning has been recently successfully applied to this problem by mapping raw sEMG signals into kinematics. Nonetheless, the optimal number of EMG signals and the type of pre-processing that would maximize performance have not been investigated yet.Approach.Here, we analyze the impact of these factors on the accuracy in kinematics estimates. For this purpose, we processed monopolar sEMG signals that were originally recorded from 320 electrodes over the forearm muscles of 13 subjects. We used a previously published deep learning method that can map the kinematics of the human hand with real-time resolution.Main results.While myocontrol algorithms essentially use the temporal envelope of the EMG signal as the only EMG feature, we show that our approach requires the full bandwidth of the signal in the temporal domain for accurate estimates. Spatial filtering however, had a smaller impact and low-order spatial filters may be suitable. Moreover, reducing the number of channels by ablation resulted in large performance losses. The highest accuracy was reached with the highest number of available sensors (n = 320). Importantly and unexpected, our results suggest that increasing the number of channels above those used in this study may further enhance the accuracy in predicting the kinematics of the human hand.Significance.We conclude that full bandwidth high-density EMG systems of hundreds of electrodes are needed for accurate kinematic estimates of the human hand.

Adaptive HD-sEMG decomposition: towards robust real-time decoding of neural drive

Objective. Neural interfacing via decomposition of high-density surface electromyography (HD-sEMG) should be robust to signal non-stationarities incurred by changes in joint pose and contraction intensity.Approach. We present an adaptive real-time motor unit decoding algorithm and test it on HD-sEMG collected from the extensor carpi radialis brevis during isometric contractions over a range of wrist angles and contraction intensities. The performance of the algorithm was verified using high-confidence benchmark decompositions derived from concurrently recorded intramuscular electromyography.Main results. In trials where contraction conditions between the initialization and testing data differed, the adaptive decoding algorithm maintained significantly higher decoding accuracies when compared to static decoding methods.Significance. Using "gold standard" verification techniques, we demonstrate the limitations of filter re-use decoding methods and show the necessity of parameter adaptation to achieve robust neural decoding.

Physics-Informed Deep Learning for Muscle Force Prediction With Unlabeled sEMG Signals

Computational biomechanical analysis plays a pivotal role in understanding and improving human movements and physical functions. Although physics-based modeling methods can interpret the dynamic interaction between the neural drive to muscle dynamics and joint kinematics, they suffer from high computational latency. In recent years, data-driven methods have emerged as a promising alternative due to their fast execution speed, but label information is still required during training, which is not easy to acquire in practice. To tackle these issues, this paper presents a novel physics-informed deep learning method to predict muscle forces without any label information during model training. In addition, the proposed method could also identify personalized muscle-tendon parameters. To achieve this, the Hill muscle model-based forward dynamics is embedded into the deep neural network as the additional loss to further regulate the behavior of the deep neural network. Experimental validations on the wrist joint from six healthy subjects are performed, and a fully connected neural network (FNN) is selected to implement the proposed method. The predicted results of muscle forces show comparable or even lower root mean square error (RMSE) and higher coefficient of determination compared with baseline methods, which have to use the labeled surface electromyography (sEMG) signals, and it can also identify muscle-tendon parameters accurately, demonstrating the effectiveness of the proposed physics-informed deep learning method.

User Training With Error Augmentation for sEMG-Based Gesture Classification

We designed and tested a system for real-time control of a user interface by extracting surface electromyographic (sEMG) activity from eight electrodes in a wristband configuration. sEMG data were streamed into a machine-learning algorithm that classified hand gestures in real-time. After an initial model calibration, participants were presented with one of three types of feedback during a human-learning stage: veridical feedback, in which predicted probabilities from the gesture classification algorithm were displayed without alteration; modified feedback, in which we applied a hidden augmentation of error to these probabilities; and no feedback. User performance was then evaluated in a series of minigames, in which subjects were required to use eight gestures to manipulate their game avatar to complete a task. Experimental results indicated that relative to the baseline, the modified feedback condition led to significantly improved accuracy. Class separation also improved, though this trend was not significant. These findings suggest that real-time feedback in a gamified user interface with manipulation of feedback may enable intuitive, rapid, and accurate task acquisition for sEMG-based gesture recognition applications.

Deep Learning for Enhanced Prosthetic Control: Real-Time Motor Intent Decoding for Simultaneous Control of Artificial Limbs

The development of advanced prosthetic devices that can be seamlessly used during an individual's daily life remains a significant challenge in the field of rehabilitation engineering. This study compares the performance of deep learning architectures to shallow networks in decoding motor intent for prosthetic control using electromyography (EMG) signals. Four neural network architectures, including a feedforward neural network with one hidden layer, a feedforward neural network with multiple hidden layers, a temporal convolutional network, and a convolutional neural network with squeeze-and-excitation operations were evaluated in real-time, human-in-the-loop experiments with able-bodied participants and an individual with an amputation. Our results demonstrate that deep learning architectures outperform shallow networks in decoding motor intent, with representation learning effectively extracting underlying motor control information from EMG signals. Furthermore, the observed performance improvements by using deep neural networks were consistent across both able-bodied and amputee participants. By employing deep neural networks instead of a shallow network, more reliable and precise control of a prosthesis can be achieved, which has the potential to significantly enhance prosthetic functionality and improve the quality of life for individuals with amputations.

EMG-based Multi-User Hand Gesture Classification via Unsupervised Transfer Learning Using Unknown Calibration Gestures

The poor generalization performance and heavy training burden of the gesture classification model contribute as two main barriers that hinder the commercialization of sEMG-based human-machine interaction (HMI) systems. To overcome these challenges, eight unsupervised transfer learning (TL) algorithms developed on the basis of convolutional neural networks (CNNs) were explored and compared on a dataset consisting of 10 gestures from 35 subjects. The highest classification accuracy obtained by CORrelation Alignment (CORAL) reaches more than 90%, which is 10% higher than the methods without using TL. In addition, the proposed model outperforms 4 common traditional classifiers (KNN, LDA, SVM, and Random Forest) using the minimal calibration data (two repeated trials for each gesture). The results also demonstrate the model has a great transfer robustness/flexibility for cross-gesture and cross-day scenarios, with an accuracy of 87.94% achieved using calibration gestures that are different with model training, and an accuracy of 84.26% achieved using calibration data collected on a different day, respectively. As the outcomes confirm, the proposed CNN TL method provides a practical solution for freeing new users from the complicated acquisition paradigm in the calibration process before using sEMG-based HMI systems.

Stimulated Single Fiber Electromyography (SFEMG) for Assessing Neuromuscular Junction Transmission in Rodent Models

As the final connection between the nervous system and muscle, transmission at the neuromuscular junction (NMJ) is crucial for normal motor function. Single fiber electromyography (SFEMG) is a clinically relevant and sensitive technique that measures single muscle fiber action potential responses during voluntary contractions or nerve stimulations to assess NMJ transmission. The assessment and quantification of NMJ transmission involves two parameters: jitter and blocking. Jitter refers to the variability in timing (latency) between consecutive single-fiber action potentials (SFAPs). Blocking signifies the failure of NMJ transmission to initiate an SFAP response. Although SFEMG is a well-established and sensitive test in clinical settings, its application in preclinical research has been relatively infrequent. This report outlines the steps and criteria employed in performing stimulated SFEMG to quantify jitter and blocking in rodent models. This technique can be used in preclinical and clinical studies to gain insights into NMJ function in the context of health, aging, and disease.

Repeated L5 Nerve Root Compromise Detected with Motor Evoked Potentials (MEP), but Not Electromyography (EMG): A Case Report

We report a case where neuromonitoring, using motor evoked potentials (MEP), detected an intraoperative L5 nerve root deficit during a lumbosacral decompression and instrumented fusion procedure. Critically, the MEP changes were not preceded nor accompanied by any significant spontaneous electromyography (sEMG) activity. Presumptive L5 innervated muscles, including tibialis anterior (TA), extensor hallucis longus (EHL) and gluteus maximus, were targets for nerve root surveillance using combined MEP and sEMG techniques. During a high-grade spondylolisthesis correction procedure, attempts to align a left-sided rod resulted in repeated loss and recovery cycles of MEP from the TA and EHL. No accompanying EMG alerts were associated with any of the MEP changes nor were MEP variations seen from muscles innervated above and below L5. After several attempts, the rod alignment was achieved, but significant MEP signal decrement (72% decrease) remained from the EHL. Postoperatively, the patient experienced significant foot drop on the left side that recovered over a period of 3 months. This case contributes to a growing body of evidence that exclusive reliance on sEMG for spinal nerve root scrutiny can be unreliable and MEP may provide more dependable data on nerve root patency.

Multiphysics and multiscale modeling of uterine contractions: integrating electrical dynamics and soft tissue deformation with fiber orientation

The development of a comprehensive uterine model that seamlessly integrates the intricate interactions between the electrical and mechanical aspects of uterine activity could potentially facilitate the prediction and management of labor complications. Such a model has the potential to enhance our understanding of the initiation and synchronization mechanisms involved in uterine contractions, providing a more profound comprehension of the factors associated with labor complications, including preterm labor. Consequently, it has the capacity to assist in more effective preparation and intervention strategies for managing such complications. In this study, we present a computational model that effectively integrates the electrical and mechanical components of uterine contractions. By combining a state-of-the-art electrical model with the Hyperelastic Mass-Spring Model (HyperMSM), we adopt a multiphysics and multiscale approach to capture the electrical and mechanical activities within the uterus. The electrical model incorporates the generation and propagation of action potentials, while the HyperMSM simulates the mechanical behavior and deformations of the uterine tissue. Notably, our model takes into account the orientation of muscle fibers, ensuring that the simulated contractions align with their inherent directional characteristics. One noteworthy aspect of our contraction model is its novel approach to scaling the rest state of the mesh elements, as opposed to the conventional method of applying mechanical loads. By doing so, we eliminate artificial strain energy resulting from the resistance of soft tissues' elastic properties during contractions. We validated our proposed model through test simulations, demonstrating its feasibility and its ability to reproduce expected contraction patterns across different mesh resolutions and configurations. Moving forward, future research efforts should prioritize the validation of our model using robust clinical data. Additionally, it is crucial to refine the model by incorporating a more realistic uterus model derived from medical imaging. Furthermore, applying the model to simulate the entire childbirth process holds immense potential for gaining deeper insights into the intricate dynamics of labor.