High-density Surface Electromyography Signals Research Articles

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.

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.

Extensor digitorum communis compartments with a double compensation synergy strategy for constant force control of the index finger.

Precise sustained force control of the fingers is important for achieving flexible hand movements. However, how neuromuscular compartments within a forearm multi-tendon muscle cooperate to achieve constant finger force remains unclear. This study aimed to investigate the coordination strategies across multiple compartments of the extensor digitorum communis (EDC) during index finger sustained constant extension. Nine subjects performed index finger extensions of 15%, 30%, and 45% maximal voluntary contractions, respectively. High-density surface electromyography signals were recorded from the EDC and then analyzed using non-negative matrix decomposition to extract activation patterns and coefficient curves of EDC compartments. The results showed two activation patterns with stable structures during all tasks: one pattern corresponding to the index finger compartment was named master pattern; whereas the other corresponding to other compartments was named auxiliary pattern. Further, the intensity and stability of their coefficient curves were assessed using the root mean square value (RMS) and coefficient of variation (CV). The RMS and CV values of the master pattern increased and decreased with time, respectively, while the corresponding values of the auxiliary pattern were both negatively correlated with the formers. These findings suggested a special coordination strategy across EDC compartments during index finger constant extension, manifesting as two compensations of the auxiliary pattern for the intensity and stability of the master pattern. The proposed method provides new insight into the synergy strategy across multiple compartments within a forearm multi-tendon during sustained isometric contraction of a single finger and a new approach for constant force control of prosthetic hands.

The effects of α-lactalbumin supplementation and handgrip contraction on soleus motoneuron excitability

We tested two strategies that hypothetically increase serotonin availability (α-lactalbumin consumption and a remote submaximal handgrip contraction) on estimates of persistent inward currents (PICs) amplitude of soleus muscle in healthy participants. With a randomised, double-blind, and cross-over design, 13 healthy participants performed triangular-shaped ramp contractions with their plantar flexors (20% of maximal torque), followed by a 30-s handgrip sustained contraction (40% of maximal force) and consecutive repeated triangular-shaped contractions. This was performed before and after the consumption of either 40g of α-lactalbumin, an isonitrogenous beverage (Zein) or an isocaloric beverage (Corn-starch). Soleus motor units discharge rates were analysed from high-density surface electromyography signals. PICs were estimated by calculating the delta frequency (ΔF) of motor unit train spikes using the paired motor unit technique. ΔF (0.19 pps; p = 0.001; d = 0.30) and peak discharge rate (0.20 pps; p < 0.001; d = 0.37) increased after the handgrip contraction, irrespective of the consumed supplement. No effects of α-lactalbumin were observed. Our results indicate that 40g of α-lactalbumin was unable to modify intrinsic motoneuron excitability. However, performing a submaximal handgrip contraction before the plantar flexion triangular contraction was capable of increasing ΔF and discharge rates on soleus motor units. These findings highlight the diffused effects of serotonergic input, its effects on motoneuron discharge behaviour, and suggest a cross-effector effect within human motoneurons.

Reliability of high-density surface electromyography for assessing characteristics of the thoracic erector spinae during static and dynamic tasks

PurposeTo establish intra- and inter-session reliability of high-density surface electromyography (HDEMG)-derived parameters from the thoracic erector spinae (ES) during static and dynamic goal-directed voluntary movements of the trunk, and during functional reaching tasks. MethodsTwenty participants performed: 1) static trunk extension, 2) dynamic trunk forward and lateral flexion, and 3) multidirectional functional reaching tasks on two occasions separated by 7.5 ± 1.2 days. Muscle activity was recorded bilaterally from the thoracic ES. Root mean square (RMS), coordinates of the barycentre, mean frequency (MNF), and entropy were derived from the HDEMG signals. Reliability was determined with intraclass correlation coefficient (ICC), coefficient of variation, and standard error of measurement. ResultsGood-to-excellent intra-session reliability was found for all parameters and tasks (ICC: 0.79-0.99), whereas inter-session reliability varied across tasks. Static tasks demonstrated higher reliability in most parameters compared to functional and dynamic tasks. Absolute RMS and MNF showed the highest overall reliability across tasks (ICC: 0.66-0.98), while reliability of the barycentre was influenced by the direction of the movements. ConclusionRMS and MNF derived from HDEMG show consistent inter-session reliability in goal-directed voluntary movements of the trunk and reaching tasks, whereas the measures of the barycentre and entropy demonstrate task-dependent reliability.

Neural decoding from surface high-density EMG signals: influence of anatomy and synchronization on the number of identified motor units

Objective. High-density surface electromyography (HD-sEMG) allows the reliable identification of individual motor unit (MU) action potentials. Despite the accuracy in decomposition, there is a large variability in the number of identified MUs across individuals and exerted forces. Here we present a systematic investigation of the anatomical and neural factors that determine this variability. Approach. We investigated factors of influence on HD-sEMG decomposition, such as synchronization of MU discharges, distribution of MU territories, muscle-electrode distance (MED—subcutaneous adipose tissue thickness), maximum anatomical cross-sectional area (ACSAmax), and fiber cross-sectional area. For this purpose, we recorded HD-sEMG signals, ultrasound and magnetic resonance images, and took a muscle biopsy from the biceps brachii muscle from 30 male participants drawn from two groups to ensure variability within the factors—untrained-controls (UT = 14) and strength-trained individuals (ST = 16). Participants performed isometric ramp contractions with elbow flexors (at 15%, 35%, 50% and 70% maximum voluntary torque—MVT). We assessed the correlation between the number of accurately detected MUs by HD-sEMG decomposition and each measured parameter, for each target force level. Multiple regression analysis was then applied. Main results. ST subjects showed lower MED (UT = 5.1 ± 1.4 mm; ST = 3.8 ± 0.8 mm) and a greater number of identified MUs (UT: 21.3 ± 10.2 vs ST: 29.2 ± 11.8 MUs/subject across all force levels). The entire cohort showed a negative correlation between MED and the number of identified MUs at low forces (r = −0.6, p = 0.002 at 15% MVT). Moreover, the number of identified MUs was positively correlated to the distribution of MU territories (r = 0.56, p = 0.01) and ACSAmax (r = 0.48, p = 0.03) at 15% MVT. By accounting for all anatomical parameters, we were able to partly predict the number of decomposed MUs at low but not at high forces. Significance. Our results confirmed the influence of subcutaneous tissue on the quality of HD-sEMG signals and demonstrated that MU spatial distribution and ACSAmax are also relevant parameters of influence for current decomposition algorithms.

High-density Surface Electromyography as Biomarker of Muscle Aging.

Sarcopenia is a muscle disease with adverse changes that increase throughout the lifetime but with different chronological scales between individuals. Addressing "early muscle aging" is becoming a critical issue for prevention. Through the CHRONOS study, we demonstrated the ability of the high-density surface electromyography (HD-sEMG), a noninvasive, wireless, portable technology, to detect both healthy muscle aging and accelerated muscle aging related to a sedentary lifestyle, one of the risk factors of sarcopenia. The HD-sEMG signals were analyzed in 91 healthy young, middle-aged, and old subjects (25-75 years) distributed according to their physical activity status (82 active and 9 sedentary; International Physical Activity Questionnaire) and compared with current methods for muscle evaluation, including muscle mass (dual-energy X-ray absorptiometry [DXA], ultrasonography), handgrip strength, and physical performance. The HD-sEMG signals were recorded from the rectus femoris during sit-to-stand trials, and 2 indexes were analyzed: muscular contraction intensity and muscle contraction dynamics. The clinical parameters did not differ significantly across the aging and physical activity levels. Inversely, the HD-sEMG indexes were correlated to age and were different significantly through the age categories of the 82 active subjects. They were significantly different between sedentary subjects aged 45-54 years and active ones at the same age. The HD-sEMG indexes of sedentary subjects were not significantly different from those of older active subjects (≥55 years). The muscle thicknesses evaluated using ultrasonography were significantly different between the 5 age decades but did not show a significant difference with physical activity. The HD-sEMG technique can assess muscle aging and physical inactivity-related "early aging," outperforming clinical and DXA parameters.

Detecting motor unit abnormalities in amyotrophic lateral sclerosis using high-density surface EMG

ObjectiveThe purpose of this study was to detect specific motor unit (MU) abnormalities in people with amyotrophic lateral sclerosis (ALS) compared to controls using high-density surface electromyography (HD-SEMG). MethodsSixteen people with ALS and 16 control subjects. The participants performed ramp up and sustained contractions at 30% of their maximal voluntary contraction. HD-SEMG signals were recorded in the vastus lateralis muscle and decomposed into individual MU firing behavior using a convolution blind source separation method. ResultsIn total, 339 MUs were detected (people with ALS; n = 93, control subjects; n = 246). People with ALS showed significantly higher mean firing rate, recruitment threshold, coefficient of variation of the MU firing rate, MU firing rate at recruitment, and motoneurons excitability than those of control subjects (p < 0.001). The number of MU, MU firing rate, recruitment threshold, and MU firing rate at recruitment were significantly correlated with disease severity (p < 0.001). Multivariable analysis revealed that an increased MU firing rate at recruitment was independently associated with ALS. ConclusionsThese results suggest increased excitability at recruitment, which is consistent with neurodegeneration results in a compensatory increase in MU activity. SignificanceAbnormal MU firing behavior provides an important physiological index for understanding the pathophysiology of ALS.