High-density Surface Electromyography Signals Research Articles

Changes in motor unit firing pattern are associated with post-exercise blood pressure response in older untreated but not treated hypertensive adults.

This study aimed to determine the difference in motor unit (MU) firing pattern between hypertensive and normotensive individuals, and the relationship between MU firing pattern and post-exercise blood pressure (BP) response in older individuals. Fourteen older untreated (systolic/diastolic BP (SBP/DBP)≥130/80mmHg, 76 (5) years), 11 treated hypertensive (78 (4) years) and 14 normotensive (SBP/DBP<130/80mmHg, 71 (4) years) individuals were studied. Participants performed ramp-up exercises until 50% of maximal voluntary contraction (MVC) of knee extension and five MVCs. During the ramp-up exercise, high-density surface electromyography signals were recorded and each MU firing rate (FR) and recruitment threshold was assessed. The slope of the linear regression between MUFRs and recruitment thresholds was calculated to assess the MU firing pattern. Pre- and post-exercise blood pressure was measured. Change in (∆)SBP from pre- to post-exercise was greater in treated hypertensive than untreated hypertensive individuals (P=0.026). MUFR was lower in treated hypertensive than untreated hypertensive and normotensive individuals (P<0.001). Although the slope was not significantly different between groups (P=0.294), FRs of larger MUs were lower than those of smaller MUs in treated hypertensive and normotensive individuals (P<0.05) but sustained in untreated hypertensive individuals. The FRs of larger MUs and slope were positively correlated with the ∆SBP only in hypertensive individuals (r=0.768 and 0.715; P=0.044 and 0.020). MUFR was lower in treated hypertensive than untreated hypertensive and normotensive individuals. Furthermore, MU firing patterns were associated with the ∆SBP after exercise in older untreated hypertensive individuals, but this relationship was not observed in treated hypertensive and normotensive individuals.

Eccentric exercise-induced delayed onset trunk muscle soreness alters high-density surface EMG-torque relationships and lumbar kinematics

We aimed to assess high-density surface electromyography (HDsEMG)-torque relationships in the presence of delayed onset trunk muscle soreness (DOMS) and the effect of these relationships on torque steadiness (TS) and lumbar movement during concentric/eccentric submaximal trunk extension contractions. Twenty healthy individuals attended three laboratory sessions (24 h apart). HDsEMG signals were recorded unilaterally from the thoracolumbar erector spinae with two 64-electrode grids. HDsEMG-torque signal relationships were explored via coherence (0–5 Hz) and cross-correlation analyses. Principal component analysis was used for HDsEMG-data dimensionality reduction and improvement of HDsEMG-torque-based estimations. DOMS did not reduce either concentric or eccentric trunk extensor muscle strength. However, in the presence of DOMS, improved TS, alongside an altered HDsEMG-torque relationship and kinematic changes were observed, in a contraction-dependent manner. For eccentric trunk extension, improved TS was observed, with greater lumbar flexion movement and a reduction in δ-band HDsEMG-torque coherence and cross-correlation. For concentric trunk extensions, TS improvements were observed alongside reduced thoracolumbar sagittal movement. DOMS does not seem to impair the ability to control trunk muscle force, however, perceived soreness induced changes in lumbar movement and muscle recruitment strategies, which could alter motor performance if the exposure to pain is maintained in the long term.

Immediate effect of local vibration on motor unit firing behavior and muscle strength in healthy young adult males.

The aim of this study was to examine the effect of vibration on motor unit (MU) firing behavior and physical performance of antagonist muscles in healthy young adult males. Fourteen males (age = 24.3 ± 3.6years) were included in this study. There were two conditions, one in which participants received 80Hz vibration in the distal tendon of the hamstring for 30s and the control condition (no vibration). High-density surface electromyography (HD-SEMG) signals and maximal voluntary contraction (MVC) of knee extensor muscles were evaluated before and after the respective conditions and recorded from the vastus lateralis muscle during submaximal ramp-up and sustained contractions at 30% MVC. Convolution blind source separation was used to decompose the HD-SEMG signals into individual MU firing behaviors. In total, 739 MUs were detected (control; 360 MUs and vibration; 379 MUs), and a total of 312 matched MUs were identified across both submaximal contraction conditions (control: 150 MUs; vibration: 162 MUs). Vibration significantly increased the discharge rate (p = 0.047) and decreased the recruitment threshold before and after intervention (p = 0.001) but not in the control condition. Furthermore, the recruitment threshold is a factor that influences discharge rate. Significant correlations were observed between the recruitment threshold and both the ∆ discharge rate and the ∆ recruitment threshold under the vibration condition (p < 0.001). Vibration increased in the discharge rate and decreased the recruitment threshold of the antagonist muscle. These findings suggested that vibration contributes to immediate changes in the neural control of antagonist muscles.

Gaussianity Evaluation of HD-sEMG Signals with Aging and Sex During Low and Moderate Isometric Contractions of the Biceps Brachii

IntroductionAging is associated with muscle decline, which alters both functional and anatomical properties of the neuromuscular system. These modifications can be reflected in high-density surface electromyography (HD-sEMG) signals. This study examines how age and sex impact the shape of the amplitude Probability Density Function (PDF) of HD-sEMG signals. Materials and MethodsMonopolar HD-sEMG signals were collected from the Biceps Brachii in a cohort of 17 individuals: 10 women (mean age: 22.9 ± 3.6 years) and 7 men (mean age: 24.4 ± 2.5 years) in the younger group, and 10 women (mean age: 69.8 ± 4.8 years) and 7 men (mean age: 72.8 ± 2.7 years) in the elderly group. The recordings were conducted during an elbow flexion at both 20% and 40% maximum voluntary contraction. The signal amplitude was evaluated using root means square amplitude (RMSA) and the PDF shape of each HD-sEMG signal was assessed through skewness, excess Kurtosis, and robust functional statistics. These shape distance metrics evaluate the departure from Gaussianity related to muscle aging. a) We conducted a comparison study of the HD-sEMG PDF shapes between younger and elderly individuals. b) Evaluating differences between men and women. c) Considering monopolar and Laplacian electrode configurations that are sensitive to different muscle regions. ResultsA) The HD-sEMG PDFs of elderly subjects demonstrated a lower departure from Gaussianity than their younger counterparts. B) Women exhibited lower RMSA values than men, and, on average, a lower departure from Gaussianity whatever the age and contraction level C) Trends of departure from Gaussianity with contraction level, seems to be influenced by the electrode configuration. In fact, a decrease in Gaussianity departure is observed with monopolar recordings where an increase is observed with Laplacian one, clearly indicating different muscle region assessment. DiscussionThe findings highlight the influence of factors such aging, sex, contraction level and electrode montage on the shape of the HD-sEMG PDF, emphasizing the significance of using this descriptor for monitoring and better assessment of muscle aging.

Motor unit discharge rate modulation during isometric contractions to failure is intensity- and modality-dependent.

The physiological mechanisms determining the progressive decline in the maximal muscle torque production capacity during isometric contractions to task failure are known to depend on task demands. Task-specificity of the associated adjustments in motor unit discharge rate (MUDR), however, remains unclear. This study examined MUDR adjustments during different submaximal isometric knee extension tasks to failure. Participants performed a sustained and an intermittent task at 20% and 50% of maximal voluntary torque (MVT), respectively (Experiment 1). High-density surface EMG signals were recorded from vastus lateralis (VL) and medialis (VM) and decomposed into individual MU discharge timings, with the identified MUs tracked from recruitment to task failure. MUDR was quantified and normalised to intervals of 10% of contraction time (CT). MUDR of both muscles exhibited distinct modulation patterns in each task. During the 20% MVT sustained task, MUDR decreased until ∼50% CT, after which it gradually returned to baseline. Conversely, during the 50% MVT intermittent task, MUDR remained stable until ∼40-50% CT, after which it started to continually increase until task failure. To explore the effect of contraction intensity on the observed patterns, VL and VM MUDR was quantified during sustained contractions at 30% and 50% MVT (Experiment 2). During the 30% MVT sustained task, MUDR remained stable until ∼80-90% CT in both muscles, after which it continually increased until task failure. During the 50% MVT sustained task the increase in MUDR occurred earlier, after ∼70-80% CT. Our results suggest that adjustments in MUDR during submaximal isometric contractions to failure are contraction modality- and intensity-dependent. KEY POINTS: During prolonged muscle contractions a constant motor output can be maintained by recruitment of additional motor units and adjustments in their discharge rate. Whilst contraction-induced decrements in neuromuscular function are known to depend on task demands, task-specificity of motor unit discharge behaviour adjustments is still unclear. In this study, we tracked and compared discharge activity of several concurrently active motor units in the vastii muscles during different submaximal isometric knee extension tasks to failure, including intermittent vs. sustained contraction modalities performed in the same intensity domain (Experiment 1), and two sustained contractions performed at different intensities (Experiment 2). During each task, motor units modulated their discharge rate in a distinct, biphasic manner, with the modulation pattern depending on contraction intensity and modality. These results provide insight into motoneuronal adjustments during contraction tasks posing different demands on the neuromuscular system.

Time-of-day effects on motor unit firing and muscle contractile properties in humans.

Intrinsic factors related to neuromuscular function are time-of-day dependent, but diurnal rhythms in neural and muscular components of the human neuromuscular system remain unclear. The present study aimed to investigate the time-of-day effects on neural excitability and muscle contractile properties by assessing the firing properties of tracked motor units and electrically evoked twitch muscle contraction. In 15 young adults (22.9 ± 4.7 yr), neuromuscular function was measured in the morning (10:00), at noon (13:30), in the evening (17:00), and at night (20:30). Four measurements were completed within 24 h. The measurements consisted of maximal voluntary contraction (MVC) strength of knee extension, recording of high-density surface electromyography (HDsEMG) from the vastus lateralis during ramp-up contraction to 50% of MVC, and evoked twitch torque of knee extensors by electrical stimulation. Recorded HDsEMG signals were decomposed to individual motor unit firing behaviors and the same motor units were tracked among the times of day, and recruitment thresholds and firing rates were calculated. The number of detected and tracked motor units was 127. Motor unit firing rates significantly increased from morning to noon, evening, and night (P < 0.01), but there were no significant differences in recruitment thresholds among the times of day (P > 0.05). Also, there were no significant effects of time of day on evoked twitch torque (P > 0.05). Changes in the motor unit firing rate and evoked twitch torque were not significantly correlated (P > 0.05). These findings suggest that neural excitability may be affected by the time of day, but it did not accompany changes in peripheral contractile properties in a diurnal manner.NEW & NOTEWORTHY We investigated the variations of tracked motor unit firing properties and electrically evoked twitch contraction during the day within 24 h. The variation of motor unit firing rate was observed, and tracked motor unit firing rate increased at noon, in the evening, and at night compared with that in the morning. The variation in motor unit firing rate was independent of changes in twitch contraction. Motor unit firing rate may be affected by diurnal rhythms.

Common synaptic inputs and persistent inward currents of vastus lateralis motor units are reduced in older male adults

Although muscle atrophy may partially account for age-related strength decline, it is further influenced by alterations of neural input to muscle. Persistent inward currents (PIC) and the level of common synaptic inputs to motoneurons influence neuromuscular function. However, these have not yet been described in the aged human quadriceps. High-density surface electromyography (HDsEMG) signals were collected from the vastus lateralis of 15 young (mean ± SD, 23 ± 5 y) and 15 older (67 ± 9 y) men during submaximal sustained and 20-s ramped contractions. HDsEMG signals were decomposed to identify individual motor unit discharges, from which PIC amplitude and intramuscular coherence were estimated. Older participants produced significantly lower knee extensor torque (p < 0.001) and poorer force tracking ability (p < 0.001) than young. Older participants also had lower PIC amplitude (p = 0.001) and coherence estimates in the alpha frequency band (p < 0.001) during ramp contractions when compared to young. Persistent inward currents and common synaptic inputs are lower in the vastus lateralis of older males when compared to young. These data highlight altered neural input to the clinically and functionally important quadriceps, further underpinning age-related loss of function which may occur independently of the loss of muscle mass.

Changes in Spatiotemporal Variability of Muscular Response under the Influence of Post-stroke Spasticity.

Spasticity is characterized by involuntary muscle activation due to the hyperexcitability of the stretch reflex. However, the temporal pattern and spatial heterogeneity of motor unit recruitment in this activation are not fully understood. The aim of this study was to investigate changes in spatiotemporal variability in spastic muscle responses to passive stretch after stroke. We acquired high-density surface electromyography (HD-sEMG) signals from the biceps brachii in 21 stroke survivors, 10 age-matched old healthy controls, and 10 young healthy controls at velocities of 10°/s, 60°/s, 120°/s, and 180°/s. The average and distribution of fuzzy entropy extracted from HD-sEMG signals were used to characterize the spatiotemporal variability of muscle activation. Our results showed that the temporal variability of stroke survivors was significantly lower than that of old controls, while the latter was significantly lower than that of young controls. As velocity increased, only stroke survivors showed decreased activation variability and increased activation levels in specific region, and the distributions were similar across velocities. Notably, the activation variability and the size of regions with high activation levels were significantly negatively and positively correlated with the severity of spasticity, respectively. These findings provide entropy-based evidence for the contribution of greater silencing of motoneurons responsible for reflex inhibition to the synchronous recruitment of more motor units.

HDE-Array: Development and Validation of a New Dry Electrode Array Design to Acquire HD-sEMG for Hand Position Estimation.

This paper aims to introduce HDE-Array (High-Density Electrode Array), a novel dry electrode array for acquiring High-Density surface electromyography (HD-sEMG) for hand position estimation through RPC-Net (Recursive Prosthetic Control Network), a neural network defined in a previous study. We aim to demonstrate the hypothesis that the position estimates returned by RPC-Net using HD-sEMG signals acquired with HDE-Array are as accurate as those obtained from signals acquired with gel electrodes. We compared the results, in terms of precision of hand position estimation by RPC-Net, using signals acquired by traditional gel electrodes and by HDE-Array. As additional validation, we performed a variance analysis to confirm that the presence of only two rows of electrodes does not result in an excessive loss of information, and we characterized the electrode-skin impedance to assess the effects of the voltage divider effect and power line interference. Performance tests indicated that RPC-Net, used with HDE-Array, achieved comparable or superior results to those observed when used with the gel electrode setup. The dry electrodes demonstrated effective performance even with a simplified setup, highlighting potential cost and usability benefits. These results suggest improvements in the accessibility and user-friendliness of upper-limb rehabilitation devices and underscore the potential of HDE-Array and RPC-Net to revolutionize control for medical and non-medical applications.

Tutorial: Analysis of central and peripheral motor unit properties from decomposed High-Density surface EMG signals with openhdemg

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

Extracting Individual Muscle Drive and Activity From High-Density Surface Electromyography Signals Based on the Center of Gravity of Motor Unit.

Neural interfacing has played an essential role in advancing our understanding of fundamental movement neurophysiology and the development of human-machine interface. However, direct neural interfaces from brain and nerve recording are currently limited in clinical areas for their invasiveness and high selectivity. Here, we applied the surface electromyogram (EMG) in studying the neural control of movement and proposed a new non-invasive way of extracting neural drive to individual muscles. Sixteen subjects performed isometric contractions to complete six hand tasks. High-density surface EMG signals (256 channels in total) recorded from the forearm muscles were decomposed into motor unit firing trains. The location of each decomposed motor unit was represented by its center of gravity and was put into clustering for distinct muscle regions. All the motor units in the same cluster served as a muscle-specific motor pool from which individual muscle drive could be extracted directly. Moreover, we cross-validated the self-clustered muscle regions by magnetic resonance imaging (MRI) recorded from the subjects' forearms. All motor units that fall within the MRI region are considered correctly clustered. We achieved a clustering accuracy of 95.72% ± 4.01% for all subjects. We provided a new framework for collecting experimental muscle-specific drives and generalized the way of surface electrode placement without prior knowledge of the targeting muscle architecture.

Motor unit tracking using blind source separation filters and waveform cross-correlations: reliability under physiological and pharmacological conditions.

Recent advancements in the analysis of high-density surface electromyography (HDsEMG) have enabled the identification, and tracking, of motor units (MUs) to study muscle activation. This study aimed to evaluate the reliability of MU tracking using two common methods: blind source separation filters and two-dimensional waveform cross-correlation. An experiment design was developed to assess physiological reliability, and reliability for a drug intervention known to reduce the firing rate of motoneurones (cyproheptadine). HDsEMG signals were recorded from tibialis anterior during isometric dorsiflexions to 10%, 30%, 50% and 70% of maximal voluntary contraction. MUs were matched within session (2 hr) using the filter method, and between sessions (7 days) via the waveform method. Both tracking methods demonstrated similar reliability during physiological conditions (e.g., MU discharge: filter ICC 10% of MVC = 0.76, to 70% of MVC = 0.86; waveform ICC: 10% of MVC = 0.78, to 70% of MVC = 0.91). Although reliability slightly reduced after the pharmacological intervention, there were no discernible differences in tracking performance (e.g., MU disc filter ICC: 10% of MVC = 0.73, to 70% of MVC = 0.75; DR waveform ICC: 10% of MVC = 0.84, to 70% of MVC = 0.85). The poorest reliability typically occurred at higher contraction intensities, which aligned with the greatest variability in MU characteristics. This study confirms that tracking method may not impact the interpretation of MU data, provided that an appropriate experiment design is employed. However, caution should be used when tracking MUs during higher intensity isometric contractions.

Decoding firings of a large population of human motor units from high-density surface electromyogram in response to transcranial magnetic stimulation.

We describe a novel application of methodology for high-density surface electromyography (HDsEMG) decomposition to identify motor unit (MU) firings in response to transcranial magnetic stimulation (TMS). The method is based on the MU filter estimation from HDsEMG decomposition with convolution kernel compensation during voluntary isometric contractions and its application to contractions elicited by TMS. First, we simulated synthetic HDsEMG signals during voluntary contractions followed by simulated motor evoked potentials (MEPs) recruiting an increasing proportion of the motor pool. The estimation of MU filters from voluntary contractions and their application to elicited contractions resulted in high (>90%) precision and sensitivity of MU firings during MEPs. Subsequently, we conducted three experiments in humans. From HDsEMG recordings in first dorsal interosseous and tibialis anterior muscles, we demonstrated an increase in the number of identified MUs during MEPs evoked with increasing stimulation intensity, low variability in the MU firing latency and a proportion of MEP energy accounted for by decomposition similar to voluntary contractions. A negative relationship between the MU recruitment threshold and the number of identified MU firings was exhibited during the MEP recruitment curve, suggesting orderly MU recruitment. During isometric dorsiflexion we also showed a negative association between voluntary MU firing rate and the number of firings of the identified MUs during MEPs, suggesting a decrease in the probability of MU firing during MEPs with increased background MU firing rate. We demonstrate accurate identification of a large population of MU firings in a broad recruitment range in response to TMS via non-invasive HDsEMG recordings. KEY POINTS: Transcranial magnetic stimulation (TMS) of the scalp produces multiple descending volleys, exciting motor pools in a diffuse manner. The characteristics of a motor pool response to TMS have been previously investigated with intramuscular electromyography (EMG), but this is limited in its capacity to detect many motor units (MUs) that constitute a motor evoked potential (MEP) in response to TMS. By simulating synthetic signals with known MU firing patterns, and recording high-density EMG signals from two human muscles, we show the feasibility of identifying firings of many MUs that comprise a MEP. We demonstrate the identification of firings of a large population of MUs in the broad recruitment range, up to maximal MEP amplitude, with fewer required stimuli compared to intramuscular EMG recordings. The methodology demonstrates an emerging possibility to study responses to TMS on a level of individual MUs in a non-invasive manner.

High-Density Surface Electromyography for Swallowing Evaluation in Post-Radiation Dysphagia.

Our study aimed to investigate the feasibility of using high-density surface electromyography (HD-sEMG) for swallowing assessment by comparing the quantitative parameters and topographic patterns of HD-sEMG between post-irradiated patients and healthy individuals. Ten healthy volunteers and ten post-irradiated nasopharyngeal carcinoma patients were recruited. 96-channel HD-sEMG was recorded although each participant consumed different consistencies of food (thin and thick liquid, puree, congee, and soft rice). Dynamic topography was generated from the root mean square (RMS) of the HD-sEMG signals to illustrate the anterior neck muscle function in the swallowing process. The averaged power of muscles and the symmetry of swallowing patterns were assessed by objective parameters including average RMS, Left/Right Energy Ratio, and Left/Right Energy Difference. The study showed different swallowing patterns between patients with dysphagia and healthy individuals. The mean RMS values were higher in the patient group compared to the healthy group, but the difference was not statistically significant. Asymmetrical patterns were shown in patients with dysphagia. HD-sEMG is a promising technique that could be used to quantitatively evaluate the average power of neck muscles and the symmetry of swallowing activities in patients with swallowing difficulties. Level 3 Laryngoscope, 133:2920-2928, 2023.

Effect of postural differences on the activation of intrinsic foot muscles during ramp-up toe flexion in young men

BackgroundIntrinsic foot muscle exercises are used in clinical and sports practice to improve performance. Force generation during toe flexion is greater in the standing posture than in the sitting posture; nonetheless, the mechanism underlying the activation of intrinsic foot muscles during force generation and whether there exists a difference between these two postures still remain unclear. Research questionAre the activities of intrinsic foot muscles affected by standing and sitting postures during gradual force generation? MethodsSeventeen men participated in the laboratory based cross-sectional study. Each participant performed a force ramp-up toe flexion task from 0% to 80% of the maximal toe flexor strength (MTFS) in sitting and standing postures. High-density surface electromyography signals obtained during the task were determined by calculating the root mean square (RMS). Additionally, modified entropy and coefficient of variation (CoV) were calculated at 20–80 % MTFS for each 10 % MTFS. ResultsThe RMS between the two postures indicated an interaction effect (p < 0.01). Post-hoc analyses revealed that intrinsic foot muscle activity during the ramp-up task was significantly higher in the standing posture than in the sitting posture at 60 % MTFS (67.53 ± 15.91 vs 54.64 ± 19.28 % maximal voluntary contraction [MVC], p = 0.03), 70 % MTFS (78.11 ± 12.93 vs 63.28 ± 18.65 % MVC, p = 0.01), and 80 % MTFS (81.78 ± 14.07 vs 66.90 ± 20.32 % MVC, p = 0.02). In the standing posture, the modified entropy at 80 % MTFS was lower than that at 20 % MTFS (p = 0.03), and the CoV at 80 % MTFS was higher than that at 20 % MTFS (p = 0.03). SignificanceThese results indicated that posture selection is important for high-intensity exercises of the intrinsic foot muscles, such as resistance training. Thus, improving performance related to toe flexor strength might be more effective when conducted under adequate weight-bearing situations, such as in the standing posture

Differences in motor unit behavior during isometric contractions in patients with diabetic peripheral neuropathy at various disease severities.

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

Robust morris screening method (RMSM) for complex physiological models

Background and objectives: Morris screening sensitivity analysis (MSM) comes forth as the method needing the minimum number of model simulations to qualify the impact of input parameter variations on outputs of complex, nonlinear and overparametrized models. However, the reliability of MSM indices (mean and standard deviation) and the reproducibility of their results are rarely explored despite the input parameter tuning/identification needs. In fact, these models, such those used in medical applications as digital twins, often lie in this category and need efficient and robust tools to assess both sensitivity and reliability of the outputs to numerous input model parameters.Methods: In this study, a new Robust Morris Screening Method (RMSM) is proposed and based on new indices: the absolute median (χ*) and the median absolute deviation (ρ). The proposed RMSM approach is evaluated on a complex multi-scales neuromuscular electrophysiological model simulating HD-sEMG (high density surface electromyography) signals at the skin surface. The reliability and stability of new RMSM indicators are evaluated at different trajectories within the parameter space and compared to classical MSM results. For this purpose, We propose a new methodology for parameter screening based on the ratio ρ/χ* as a graphic indicator of (non)linearity and (non)monotonicity of parameter effects.Results: Firstly, the results demonstrated that the computed elementary effects (EE) of inputs are not normally distributed using MSM indices contrary to the proposed RMSM indices. Secondly, the ranking stability of RMSM indices was earlier obtained from 20 trajectories (T=20), while MSM ranking remained unstable until T = 100. Thirdly, The screening separation between influential and negligible input model parameters was more distinct and interpretable with RMSM than MSM.Conclusion: The proposed RMSM approach ensures a fast, reliable and stable ranking of parameters for complex and overparametrized models compared to classical MSM. this allows a more precise exploration of the model parameter influence space for future application in parameter tuning and identification.

MUNIX repeatability evaluation method based on FastICA demixing.

To enhance the reproducibility of motor unit number index (MUNIX) for evaluating neurological disease progression, this paper proposes a negative entropy-based fast independent component analysis (FastICA) demixing method to assess MUNIX reproducibility in the presence of inter-channel mixing of electromyography (EMG) signals acquired by high-density electrodes. First, composite surface EMG (sEMG) signals were obtained using high-density surface electrodes. Second, the FastICA algorithm based on negative entropy was employed to determine the orthogonal projection matrix that minimizes the negative entropy of the projected signal and effectively separates mixed sEMG signals. Finally, the proposed experimental approach was validated by introducing an interrelationship criterion to quantify independence between adjacent channel EMG signals, measuring MUNIX repeatability using coefficient of variation (CV), and determining motor unit number and size through MUNIX. Results analysis shows that the inclusion of the full (128) channel sEMG information leads to a reduction in CV value by $1.5 \pm 0.1$ and a linear decline in CV value with an increase in the number of channels. The correlation between adjacent channels in participants decreases by $0.12 \pm 0.05$ as the number of channels gradually increases. The results demonstrate a significant reduction in the number of interrelationships between sEMG signals following negative entropy-based FastICA processing, compared to the mixed sEMG signals. Moreover, this decrease in interrelationships becomes more pronounced with an increasing number of channels. Additionally, the CV of MUNIX gradually decreases with an increase in the number of channels, thereby optimizing the issue of abnormal MUNIX repeatability patterns and further enhancing the reproducibility of MUNIX based on high-density surface EMG signals.

Mapping Individual Motor Unit Activity to Continuous Three-DoF Wrist Torques: Perspectives for Myoelectric Control.

The surface electromyography (EMG) decomposition techniques provide access to motor neuron activities and have been applied to myoelectric control schemes. However, the current decomposition-based myoelectric control mainly focuses on discrete gestures or single-DoF continuous movements. In this study, we aimed to map the motor unit discharges, which were identified from high-density surface EMG, to the three degrees of freedom (DoFs) wrist movements. The 3-DoF wrist torques and high-density surface EMG signals were recorded concurrently from eight non-disabled subjects. The experimental protocol included single-DoF movements and their various combinations. We decoded the motor unit discharges from the EMG signals using a segment-wise decomposition algorithm. Then the neural features were extracted from motor unit discharges and projected to wrist torques with a multiple linear regression model. We compared the performance of two neural features (twitch model and spike counting) and two training schemes (single-DoF and multi-DoF training). On average, 145 ± 33 motor units were identified from each subject, with a pulse-to-noise ratio of 30.8 ± 4.2 dB. Both neural features exhibited high estimation accuracy of 3-DoF wrist torques, with an average [Formula: see text] of 0.76 ± 0.12 and normalized root mean square error of 11.4 ± 3.1%. These results demonstrated the efficiency of the proposed method in continuous estimation of 3-DoF wrist torques, which has the potential to advance dexterous myoelectric control based on neural information.

High-density Surface EMG Decomposition by Combining Iterative Convolution Kernel Compensation with an Energy-specific peel-off Strategy.

Objective- This study aims to develop a novel framework for high-density surface electromyography (HD-sEMG) signal decomposition with superior decomposition yield and accuracy, especially for low-energy MUs. Methods- An iterative convolution kernel compensation-peel off (ICKC-P) framework is proposed, which consists of three steps: decomposition of the motor units (MUs) with relatively large energy by using the iterative convolution kernel compensation (ICKC) method and extraction of low-energy MUs with a Post-Processor and novel 'peel-off' strategy. Results- The performance of the proposed framework was evaluated by both simulated and experimental HD-sEMG signals. Our simulation results demonstrated that, with 120 simulated MUs, the proposed framework extracts more MUs compared to K-means convolutional kernel compensation (KmCKC) approach across six noise levels. And the proposed 'peel-off' strategy estimates more accurate MUAP waveforms at six noise levels than the 'peel-off' strategy proposed in the progressive FastICA peel-off (PFP) framework. For the experimental sEMG signals recorded from biceps brachii, an average of 16.1 ± 3.4 MUs were identified from each contraction, while only 10.0 ± 2.8 MUs were acquired by the KmCKC method. Conclusion- The high yield and accuracy of MUs decomposed from simulated and experimental HD-sEMG signals demonstrate the superiority of the proposed framework in decomposing low-energy MUs compared to existing methods for HD-sEMG signal decomposition. Significance- The proposed framework enables us to construct a more representative motor unit pool, consequently enhancing our understanding pertaining to various neuropathological conditions and providing invaluable information for the diagnosis and treatment of neuromuscular disorders and motor neuron diseases.