Volume conduction models for surface EMG; confrontation with measurements

Evaluating the Relationship Between Surface and Intramuscular-Based Electromyography Signals: Implications of Subcutaneous Fat Thickness

Intramuscular (iEMG) and surface electromyographic (sEMG) signals have been compared previously using predictive regression equations, finite element modeling, and correlation and cross-correlation analyses. Although subcutaneous fat thickness (SCFT) has been identified as a primary source of sEMG signal amplitude attenuation and low-pass filter equivalence, few studies have explored the potential effect of SCFT on sEMG and iEMG signal characteristics. The purpose of this study was to investigate the relationship between normalized submaximal iEMG and sEMG signal amplitudes collected from 4 muscles (rectus femoris, vastus lateralis, infraspinatus, and erector spinae) and determine whether SCFT explains more variance in this relationship. The effect of sex was also explored. Linear regression models demonstrated that the relationship between sEMG and iEMG was highly variable across the muscles examined (adjusted coefficient of determination [Adj R2] = .02-.74). SCFT improved the model fit for vastus lateralis, although this relationship only emerged with the inclusion of sex as a covariate. Thus, this research suggests that SCFT is not a prominent factor affecting the linearity between sEMG and iEMG. Researchers should investigate other parameters that may affect the linearity between sEMG and iEMG signals.

Muscle activity mapping by single peak localization from HDsEMG

Human-machine interfaces using electromyography (EMG) offer promising applications in control of prosthetic limbs, rehabilitation assessment, and assistive technologies. These applications rely on advanced algorithms that decode the activation patterns of muscles contractions. This paper presents a new approach to assess and decode muscle activity by localizing the origin of individual temporal peaks in high-density surface EMG recordings from the dorsal forearm during low force finger extensions. Localization was performed using a surface Gaussian fit applied in the spatial domain to the varying amplitudes across the channels of the electrode grids. Localized EMG peaks were used to estimate different muscle volumes for each finger, showing high consistency across 10 subjects. The results suggest that muscle regions generating each action are highly distinct and indicate potential structural differences of muscle fibres between digits. The estimated volumes were further used to classify individual EMG peaks into each corresponding action. The percentage of correctly classified peaks for each action across 10 participants were 79 ± 18, 84 ± 9, 76 ± 13, and 79 ± 9 percent for index, middle, ring, and little finger extension, respectively. The presented volume analysis provides a new approach to assessing the spatial activation patterns in compact muscle anatomies; and the single peak classification approach opens up possibilities for near-instantaneous identification of muscle activations.

The extraction of neural strategies from the surface EMG: 2004-2024

This review follows two previous papers [Farina et al. Appl Physiol (1985) 96: 1486-1495, 2004; Farina et al. J Appl Physiol (1985) 117: 1215-1230, 2014] in which we reflected on the use of surface electromyography (EMG) in the study of the neural control of movement. This series of papers began with an analysis of the indirect approaches of EMG processing to infer the neural control strategies and then closely followed the progress in EMG technology. In this third paper, we focus on three main areas: surface EMG modeling; surface EMG processing, with an emphasis on decomposition; and interfacing applications of surface EMG recordings. We highlight the latest advances in EMG models that allow fast generation of simulated signals from realistic volume conductors, with applications ranging from validation of algorithms to identification of nonmeasurable parameters by inverse modeling. Surface EMG decomposition is currently an established state-of-the-art tool for physiological investigations of motor units. It is now possible to identify large samples of motor units, to track motor units over multiple sessions, to partially compensate for the nonstationarities in dynamic contractions, and to decompose signals in real time. The latter achievement has facilitated advances in myocontrol, by using the online decoded neural drive as a control signal, such as in the interfacing of prostheses. Looking back over the 20 yr since our first review, we conclude that the recording and analysis of surface EMG signals have seen breakthrough advances in this period. Although challenges in its application and interpretation remain, surface EMG is now a solid and unique tool for the study of the neural control of movement.

Analyzing How Skinfold Thickness Affects Log-Transformed EMG Amplitude-Power Output Metrics

BACKGROUND

The purpose of this study was to determine whether accounting for skinfold thickness would reduce the variability observed on a subject-by-subject basis for the y-intercept and slope terms derived from the log-transformed EMG amplitude-power output relationship. We hypothesized that using skinfold thickness as a covariate would reduce the subject-by-subject variability in the y-intercept and slope terms and, therefore, indicate potential mean differences between muscle groups.

METHODS

Subjects had the skinfold from their three superficial quadriceps femoris muscles measured and then EMG electrodes placed over the three muscles. Thereafter, each subject performed an incremental single-leg knee-extensor ergometer exercise test to voluntary exhaustion.

RESULTS

The results indicated that using skinfold thickness as a covariate did not change the statistical outcome when comparing the mean values for the y-intercept or slope terms across the three superficial quadriceps femoris muscles.

CONCLUSION

These findings suggest that there may be other factors that are influencing the subject-by-subject variability for the y-intercept and slope terms, respectively.

Inferring position of motor units from high-density surface EMG

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

Pain and Functional Outcomes following Targeted Muscle Reinnervation: A Systematic Review

BACKGROUND

It is estimated that by 2050, a total of 3.6 million patients will be living with an amputation in the United States. The objective of this systematic review is to evaluate the effect of targeted muscle reinnervation (TMR) on pain and physical functioning in amputees.

METHODS

A literature search was performed on PubMed, Embase, and MEDLINE up to November 28, 2021. Clinical studies assessing the outcomes of TMR (pain, prosthesis control, life quality, limb function, and disability) were included.

RESULTS

Thirty-nine articles were included. The total number of patients who underwent TMR was 449, and 716 were controls. Mean follow-up was 25 months. A total of 309 (66%) lower-limb and 159 (34%) upper-limb amputations took place in the TMR group, the most common being below-knee amputations (39%). The control group included a total of 557 (84%) lower-limb and 108 (16%) upper-limb amputations; the greatest proportion being below-knee amputations in this group as well (54%). Trauma was the most common indication for amputation. Phantom limb pain scores were lower by 10.2 points for intensity ( P = 0.01), 4.67 points for behavior ( P = 0.01), and 8.9 points for interference ( P = 0.09). Similarly, residual limb pain measures were lower for cases for intensity, behavior, and interference, but they failed to reach significance. Neuroma symptoms occurred less frequently, and functional and prosthesis control outcomes improved following TMR.

CONCLUSION

The literature evidence suggests that TMR is a promising therapy for improving pain, prosthesis use, and functional outcomes after limb amputation.

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

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

A myoelectric digital twin for fast and realistic modelling in deep learning

Muscle electrophysiology has emerged as a powerful tool to drive human machine interfaces, with many new recent applications outside the traditional clinical domains, such as robotics and virtual reality. However, more sophisticated, functional, and robust decoding algorithms are required to meet the fine control requirements of these applications. Deep learning has shown high potential in meeting these demands, but requires a large amount of high-quality annotated data, which is expensive and time-consuming to acquire. Data augmentation using simulations, a strategy applied in other deep learning applications, has never been attempted in electromyography due to the absence of computationally efficient models. We introduce a concept of Myoelectric Digital Twin - highly realistic and fast computational model tailored for the training of deep learning algorithms. It enables simulation of arbitrary large and perfectly annotated datasets of realistic electromyography signals, allowing new approaches to muscular signal decoding, accelerating the development of human-machine interfaces.

Optimizing NV magnetometry for Magnetoneurography and Magnetomyography applications

Magnetometers based on color centers in diamond are setting new frontiers for sensing capabilities due to their combined extraordinary performances in sensitivity, bandwidth, dynamic range, and spatial resolution, with stable operability in a wide range of conditions ranging from room to low temperatures. This has allowed for its wide range of applications, from biology and chemical studies to industrial applications. Among the many, sensing of bio-magnetic fields from muscular and neurophysiology has been one of the most attractive applications for NV magnetometry due to its compact and proximal sensing capability. Although SQUID magnetometers and optically pumped magnetometers (OPM) have made huge progress in Magnetomyography (MMG) and Magnetoneurography (MNG), exploring the same with NV magnetometry is scant at best. Given the room temperature operability and gradiometric applications of the NV magnetometer, it could be highly sensitive in the pT / Hz -range even without magnetic shielding, bringing it close to industrial applications. The presented work here elaborates on the performance metrics of these magnetometers to the state-of-the-art techniques by analyzing the sensitivity, dynamic range, and bandwidth, and discusses the potential benefits of using NV magnetometers for MMG and MNG applications.

HArtMuT-modeling eye and muscle contributors in neuroelectric imaging

Objective.Magneto- and electroencephalography (M/EEG) measurements record a mix of signals from the brain, eyes, and muscles. These signals can be disentangled for artifact cleaning e.g. using spatial filtering techniques. However, correctly localizing and identifying these components relies on head models that so far only take brain sources into account.Approach.We thus developed the Head Artifact Model using Tripoles (HArtMuT). This volume conduction head model extends to the neck and includes brain sources as well as sources representing eyes and muscles that can be modeled as single dipoles, symmetrical dipoles, and tripoles. We compared a HArtMuT four-layer boundary element model (BEM) with the EEGLAB standard head model on their localization accuracy and residual variance (RV) using a HArtMuT finite element model (FEM) as ground truth. We also evaluated the RV on real-world data of mobile participants, comparing different HArtMuT BEM types with the EEGLAB standard head model.Main results.We found that HArtMuT improves localization for all sources, especially non-brain, and localization error and RV of non-brain sources were in the same range as those of brain sources. The best results were achieved by using cortical dipoles, muscular tripoles, and ocular symmetric dipoles, but dipolar sources alone can already lead to convincing results.Significance.We conclude that HArtMuT is well suited for modeling eye and muscle contributions to the M/EEG signal. It can be used to localize sources and to identify brain, eye, and muscle components. HArtMuT is freely available and can be integrated into standard software.

Teaching Essential EMG Theory to Kinesiologists and Physical Therapists Using Analogies Visual Descriptions, and Qualitative Analysis of Biophysical Concepts

Electromyography (EMG) is a multidisciplinary field that brings together allied health (kinesiology and physical therapy) and the engineering sciences (biomedical and electrical). Since the physical sciences are used in the measurement of a biological process, the presentation of the theoretical foundations of EMG is most conveniently conducted using math and physics. However, given the multidisciplinary nature of EMG, a course will most likely include students from diverse backgrounds, with varying levels of math and physics. This is a pedagogical paper that outlines an approach for teaching foundational concepts in EMG to kinesiologists and physical therapists that uses a combination of analogies, visual descriptions, and qualitative analysis of biophysical concepts to develop an intuitive understanding for those who are new to surface EMG. The approach focuses on muscle fiber action potentials (MFAPs), motor unit action potentials (MUAPs), and compound muscle action potentials (CMAPs) because changes in these waveforms are much easier to identify and describe in comparison to the surface EMG interference pattern (IP).

Anomalies of motor unit amplitude and territory after botulinum toxin injection

OBJECTIVE

Botulinum toxin (BT) induced cholinergic denervation of hyperactive motor units is a clinically accepted and extensively practiced way of managing focal spasticity after stroke. The denervation potentially initiates a temporary reorganization of the motor unit (MU) structure by inducing the emergence of a large number of newly innervated muscle fibers. In this study, we quantify the effect of the BT on motor unit action potential (MUAP) amplitudes and on the motor unit territory areas (MUTA) as seen on the surface of the skin over the biceps brachii (BB) muscle.

APPROACH

We have used a 128 channel high-density electromyography (HDsEMG) grid on the spastic and contralateral BB muscle and recorded the myoelectric activity along with the contraction force during isometric contraction of elbow muscles. We have decomposed the recorded EMG signal into individual MU potentials and estimated the MUAP amplitudes and territory areas before and two weeks after a BT injection.

MAIN RESULT

We found that there were significantly larger median (47±9%) MUAP amplitudes as well as reduction of MUTA (20±2%) two weeks after the injection compared to the respective pre-injection recording.

SIGNIFICANCE

The observed covariation of the amplitude and the territory area indicates that the large amplitude MUs that appeared after the BT injection have a relatively smaller territory area. We discuss the potential contributing factors to these changes subsequent to the injection in the context of the investigated subject cohort.

Physical and electrophysiological motor unit characteristics are revealed with simultaneous high-density electromyography and ultrafast ultrasound imaging

Electromyography and ultrasonography provide complementary information about electrophysiological and physical (i.e. anatomical and mechanical) muscle properties. In this study, we propose a method to assess the electrical and physical properties of single motor units (MUs) by combining High-Density surface Electromyography (HDsEMG) and ultrafast ultrasonography (US). Individual MU firings extracted from HDsEMG were used to identify the corresponding region of muscle tissue displacement in US videos. The time evolution of the tissue velocity in the identified region was regarded as the MU tissue displacement velocity. The method was tested in simulated conditions and applied to experimental signals to study the local association between the amplitude distribution of single MU action potentials and the identified displacement area. We were able to identify the location of simulated MUs in the muscle cross-section within a 2 mm error and to reconstruct the simulated MU displacement velocity (cc > 0.85). Multiple regression analysis of 180 experimental MUs detected during isometric contractions of the biceps brachii revealed a significant association between the identified location of MU displacement areas and the centroid of the EMG amplitude distribution. The proposed approach has the potential to enable non-invasive assessment of the electrical, anatomical, and mechanical properties of single MUs in voluntary contractions.

Investigating the spatial resolution of EMG and MMG based on a systemic multi-scale model

While electromyography (EMG) and magnetomyography (MMG) are both methods to measure the electrical activity of skeletal muscles, no systematic comparison between both signals exists. Within this work, we propose a novel in silico model for EMG and MMG and test the hypothesis that MMG surpasses EMG in terms of spatial selectivity, i.e. the ability to distinguish spatially shifted sources. The results show that MMG provides a slightly better spatial selectivity than EMG when recorded directly on the muscle surface. However, there is a remarkable difference in spatial selectivity for non-invasive surface measurements. The spatial selectivity of the MMG components aligned with the muscle fibres and normal to the body surface outperforms the spatial selectivity of surface EMG. Particularly, for the MMG’s normal-to-the-surface component the influence of subcutaneous fat is minimal. Further, for the first time, we analyse the contribution of different structural components, i.e. muscle fibres from different motor units and the extracellular space, to the measurable biomagnetic field. Notably, the simulations show that for the normal-to-the-surface MMG component, the contribution from volume currents in the extracellular space and in surrounding inactive tissues, is negligible. Further, our model predicts a surprisingly high contribution of the passive muscle fibres to the observable magnetic field.

Electrodes Adaptive Model in Estimating the Depth of Motor Unit: A Motor Unit Action Potential Based Approach

High-density surface electromyography (EMG) has been proposed to overcome the lower selectivity with respect to needle EMG and to provide information on a wide area over the considered muscle. Motor units decomposed from surface EMG signal of different depths differ in the distribution of action potentials detected in the skin surface. We propose a noninvasive model for estimating the depth of motor unit. We find that the depth of motor unit is linearly related to the Gaussian RMS width fitted by data points extracted from motor unit action potential. Simulated and experimental signals are used to evaluate the model performance. The correlation coefficient between reference depth and estimated depth is 0.92 ± 0.01 for simulated motor unit action potentials. Due to the symmetric nature of our model, no significant decrease is detected during the electrode selection procedure. We further checked the estimation results from decomposed motor units, the correlation coefficient between reference depth and estimated depth is 0.82 ± 0.07. For experimental signals, high discrimination of estimated depth vector is detected across gestures among trials. These results show the potential for a straightforward assessment of depth of motor units inside muscles. We discuss the potential of a non-invasive way for the location of decomposed motor units.

Subject-Specific EMG Modeling with Multiple Muscles: A Preliminary Study

Modeling of surface electromyographic (EMG) signal has been proven valuable for signal interpretation and algorithm validation. However, most EMG models are currently limited to single muscle, either with numerical or analytical approaches. Here, we present a preliminary study of a subject-specific EMG model with multiple muscles. Magnetic resonance (MR) technique is used to acquire accurate cross section of the upper limb and contours of five muscle heads (biceps brachii, brachialis, lateral head, medial head, and long head of triceps brachii). The MR image is adjusted to an idealized cylindrical volume conductor model by image registration. High-density surface EMG signals are generated for two movements - elbow flexion and elbow extension. The simulated and experimental potentials were compared using activation maps. Similar activation zones were observed for each movement. These preliminary results indicate the feasibility of the multi-muscle model to generate EMG signals for complex movements, thus providing reliable data for algorithm validation.

Tutorial. Surface EMG detection in space and time: Best practices

This tutorial is aimed to non-engineers using, or planning to use, surface electromyography (sEMG) as an assessment tool in the prevention, monitoring and rehabilitation fields. Its first purpose is to address the issues related to the origin and nature of the signal and to its detection (electrode size, distance, location) by one-dimensional (bipolar and linear arrays) and two-dimensional (grids) electrode systems while avoiding advanced mathematical, physical or physiological issues. Its second purpose is to outline best practices and provide general guidelines for proper signal detection. Issues related to the electrode-skin interface, signal conditioning and interpretation will be discussed in subsequent tutorials.

Anatomically accurate model of EMG during index finger flexion and abduction derived from diffusion tensor imaging

This study presents a modelling framework in which information on muscle fiber direction and orientation during contraction is derived from diffusion tensor imaging (DTI) and incorporated in a computational model of the surface electromyographic (EMG) signal. The proposed model makes use of the principle of reciprocity to simultaneously calculate the electric potentials produced at the recording electrode by charges distributed along an arbitrary number of muscle fibers within the muscle, allowing for a computationally efficient evaluation of extracellular motor unit action potentials. The approach is applied to the complex architecture of the first dorsal interosseous (FDI) muscle of the hand to simulate EMG during index finger flexion and abduction. Using diffusion tensor imaging methods, the results show how muscle fiber orientation and curvature in this intrinsic hand muscle change during flexion and abduction. Incorporation of anatomically accurate muscle architecture and other hand tissue morphologies enables the model to capture variations in extracellular action potential waveform shape across the motor unit population and to predict experimentally observed differences in EMG signal features when switching from index finger abduction to flexion. The simulation results illustrate how structural and electrical properties of the tissues comprising the volume conductor, in combination with fiber direction and curvature, shape the detected action potentials. Using the model, the relative contribution of motor units of different sizes located throughout the muscle under both conditions is examined, yielding a prediction of the detection profile of the surface EMG electrode array over the muscle cross-section.

Advances in diffusion tensor imaging are providing new information on muscle architecture and the orientation of muscle fibers in vivo. The arrangement of muscle fibers, in combination with geometrical and electrical properties of the surrounding biological tissues, shapes the electrical signal recorded at the skin surface during muscle contraction. As new recording and analysis methods enable muscle and motor unit activity to be examined during complex dynamic contractions, changes in muscle fiber orientation and surrounding tissue properties pose challenges for the interpretation of these data. Here we incorporate details of tissue geometry and muscle fiber architecture obtained using anatomical and diffusion MRI into an anatomically accurate model of electromyography (EMG) signal generation in the first dorsal interosseous muscle of the hand. The new modeling approach presented integrates interdependent electrical and geometrical properties in an anatomically accurate manner, leading to a realistic EMG model where tissue electrical properties are inherently related to bioelectric aspects of muscle activation. The results show how muscle fiber orientation and curvature change according to the direction of force generation, influencing the EMG signal, and provide new insights on how constitutive, anatomical and physiological properties contribute to shape motor unit action potentials detected at the skin surface.

A Deep Convolutional Neural Network Approach to Classify Normal and Abnormal Gastric Slow Wave Initiation From the High Resolution Electrogastrogram

OBJECTIVE

Gastric slow wave abnormalities have been associated with gastric motility disorders. Invasive studies in humans have described normal and abnormal propagation of the slow wave. This study aims to disambiguate the abnormally functioning wave from one of normalcy using multi-electrode abdominal waveforms of the electrogastrogram (EGG).

METHODS

Human stomach and abdominal models are extracted from computed tomography scans. Normal and abnormal slow waves are simulated along stomach surfaces. Current dipoles at the stomachs surface are propagated to virtual electrodes on the abdomen with a forward model. We establish a deep convolutional neural network (CNN) framework to classify normal and abnormal slow waves from the multi-electrode waveforms. We investigate the effects of non-idealized measurements on performance, including shifted electrode array positioning, smaller array sizes, high body mass index (BMI), and low signal-to-noise ratio (SNR). We compare the performance of our deep CNN to a linear discriminant classifier using wave propagation spatial features.

RESULTS

A deep CNN framework demonstrated robust classification, with accuracy above 90% for all SNR above 0 dB, horizontal shifts within 3 cm, vertical shifts within 6 cm, and abdominal tissue depth within 6 cm. The linear discriminant classifier was much more vulnerable to SNR, electrode placement, and BMI.

CONCLUSION

This is the first study to attempt and, moreover, succeed in using a deep CNN to disambiguate normal and abnormal gastric slow wave patterns from high-resolution EGG data.

SIGNIFICANCE

These findings suggest that multi-electrode cutaneous abdominal recordings have the potential to serve as widely deployable clinical screening tools for gastrointestinal foregut disorders.

Methodology of surface electromyography in gait analysis: review of the literature

Gait analysis is a significant diagnostic procedure for the clinicians who manage musculoskeletal disorders. Surface electromyography (sEMG) combined with kinematic and kinetic data is a useful tool for decision making of the appropriate method needed to treat such patients. sEMG has been used for decades to evaluate neuromuscular responses during a range of activities and develop rehabilitation protocols. The sEMG methodology followed by researchers assessed the issues of noise control, wave frequency, cross talk, low signal reception, muscle co-contraction, electrode placement protocol and procedure as well as EMG signal timing, intensity and normalisation so as to collect accurate, adequate and meaningful data. Further research should be done to provide more information related to the muscle activity recorded by sEMG and the force produced by the corresponding muscle during gait analysis.

Vastus lateralis muscle tissue composition and motor unit properties in chronically endurance-trained vs. sedentary women

This study examined motor unit (MU) amplitudes (AP_AMPS) and firing rates during moderate-intensity contractions and muscle cross-sectional area (mCSA) and echo intensity (mEI) of the vastus lateralis (VL) in chronically endurance-trained and sedentary females. Eight endurance-trained (ET) and nine sedentary controls (SED) volunteered for this study. Surface electromyographic (EMG) signals from a five-pin electrode array were recorded from the VL during isometric trapezoid muscle actions at 40% of maximal voluntary contraction (MVC). Decomposition methods were applied to the EMG signals to extract the firing events and amplitudes of single MUs. The mean firing rate (MFR) during steady force and MUAP_AMP for each MU was regressed against recruitment threshold (RT, expressed as %MVC). The y-intercepts and slopes from the MFR and MUAP_AMP vs. RT relationships were calculated. EMG amplitude during steady force was normalized (N-EMG_RMS) to peak EMG amplitude recorded during the MVC. Ultrasonography was used to measure mCSA and mEI. Significant differences existed between the ET and SED for the slopes (P = 0.005, P = 0.001) from the MFR and MUAP_AMP vs. RT relationships with no differences for the y-intercepts (P > 0.05). N-EMG_RMS was significantly (P = 0.033) lower for the ET than SED. There were no differences between groups for mCSA; however, the SED possessed significantly (P = 0.001) greater mEI. Subsequently, the ET likely possessed hypertrophied and stronger MUs that allowed for lower necessary muscle activation to maintain the same relative task as the SED. The larger MUs for the ET is supported via the MFR vs. RT relationships and ultrasound data.

A simulation study on the relation between the motor unit depth and action potential from multi-channel surface electromyography recordings

To investigate the spatial information of individual motor unit (MUs) using multi-channel surface electromyography (EMG) decomposition. The K-means clustering convolution kernel compensation (KmCKC) approach was employed to detect the innervation pulse trains (IPTs) from the simulated surface EMG signals, and the motor unit action potentials (MUAPs) were evaluated using the spike-triggered average (STA) technique. The relationships between the features of MUAP and MU depth were determinated with a least square fitting method. The errors of peak-to-peak (PTP) amplitude of reconstructed MUAPs were less than 5.73%, even with 0 dB signal-to-noise (SNR). The fitting errors with nonlinear model were less than 5.55% for SNRs higher than 20 dB. The results show that it is possible to provide a useful method for estimating MU depth from surface EMG recordings. It is expected to extend the applicability of surface EMG technique to more challenging clinical applications.

Examination of muscle composition and motor unit behavior of the first dorsal interosseous of normal and overweight children

We examined differences between normal weight (NW) and overweight (OW) children aged 8–10 yr in strength, muscle composition, and motor unit (MU) behavior of the first dorsal interosseous. Ultrasonography was used to determine muscle cross-sectional area (CSA), subcutaneous fat (sFAT), and echo intensity (EI). MU behavior was assessed during isometric muscle actions at 20% and 50% of maximal voluntary contraction (MVC) by analyzing electromyography amplitude (EMGRMS) and relationships between mean firing rates (MFR), recruitment thresholds (RT), and MU action potential amplitudes (MUAPsize) and durations (MUAPtime). The OW group had significantly greater EI than the NW group ( P = 0.002; NW, 47.99 ± 6.01 AU; OW, 58.90 ± 10.63 AU, where AU is arbitrary units) with no differences between groups for CSA ( P = 0.688) or MVC force ( P = 0.790). MUAPsize was larger for NW than OW in relation to RT ( P = 0.002) and for MUs expressing similar MFRs ( P = 0.011). There were no significant differences ( P = 0.279–0.969) between groups for slopes or y-intercepts from the MFR vs. RT relationships. MUAPtime was larger in OW ( P = 0.015) and EMGRMS was attenuated in OW compared with NW ( P = 0.034); however, there were no significant correlations ( P = 0.133−0.164, r = 0.270−0.291) between sFAT and EMGRMS. In a muscle that does not support body mass, the OW children had smaller MUAPsize as well as greater EI, although anatomical CSA was similar. This contradicts previous studies examining larger limb muscles. Despite evidence of smaller MUs, the OW children had similar isometric strength compared with NW children.

NEW & NOTEWORTHY Ultrasound data and motor unit action potential sizes suggest that overweight children have poorer muscle composition and smaller motor units in the first dorsal interosseous than normal weight children. Evidence is presented that suggests differences in action potential size cannot be explained by differences in subcutaneous fat alone.

Speedup computation of HD-sEMG signals using a motor unit-specific electrical source model

Nowadays, bio-reliable modeling of muscle contraction is becoming more accurate and complex. This increasing complexity induces a significant increase in computation time which prevents the possibility of using this model in certain applications and studies. Accordingly, the aim of this work is to significantly reduce the computation time of high-density surface electromyogram (HD-sEMG) generation. This will be done through a new model of motor unit (MU)-specific electrical source based on the fibers composing the MU. In order to assess the efficiency of this approach, we computed the normalized root mean square error (NRMSE) between several simulations on single generated MU action potential (MUAP) using the usual fiber electrical sources and the MU-specific electrical source. This NRMSE was computed for five different simulation sets wherein hundreds of MUAPs are generated and summed into HD-sEMG signals. The obtained results display less than 2% error on the generated signals compared to the same signals generated with fiber electrical sources. Moreover, the computation time of the HD-sEMG signal generation model is reduced to about 90% compared to the fiber electrical source model. Using this model with MU electrical sources, we can simulate HD-sEMG signals of a physiological muscle (hundreds of MU) in less than an hour on a classical workstation. Graphical Abstract Overview of the simulation of HD-sEMG signals using the fiber scale and the MU scale. Upscaling the electrical source to the MU scale reduces the computation time by 90% inducing only small deviation of the same simulated HD-sEMG signals.

A computational model to investigate the effect of pennation angle on surface electromyogram of Tibialis Anterior

This study has described and experimentally validated the differential electrodes surface electromyography (sEMG) model for tibialis anterior muscles during isometric contraction. This model has investigated the effect of pennation angle on the simulated sEMG signal. The results show that there is no significant effect of pennation angle in the range 0° to 20° to the single fibre action potential shape recorded on the skin surface. However, the changes with respect to pennation angle are observed in sEMG amplitude, frequency and fractal dimension. It is also observed that at different levels of muscle contractions there is similarity in the relationships with Root Mean Square, Median Frequency, and Fractal Dimension of the recorded and simulated sEMG signals.

A new approach for multi-channel surface EMG signal simulation

Simulation models are necessary for testing the performance of newly developed approaches before they can be applied to interpreting experimental data, especially when biomedical signals such as surface electromyogram (SEMG) signals are involved. A new and easily implementable surface EMG simulation model was developed in this study to simulate multi-channel SEMG signals. A single fiber action potential (SFAP) is represented by the sum of three Gaussian functions. SFAP waveforms can be modified by adjusting the amplitude and bandwidth of the Gaussian functions. SEMG signals were successfully simulated at different detected locations. Effects of the fiber depth, electrode position and conduction velocity of SFAP on motor unit action potential (MUAP) were illustrated. Results demonstrate that the easily implementable SEMG simulation approach developed in this study can be used to effectively simulate SEMG signals.

Motor units in the human medial gastrocnemius muscle are not spatially localized or functionally grouped

KEY POINTS

Human medial gastrocnemius (MG) motor units (MUs) are thought to occupy small muscle territories or regions, with low-threshold units preferentially located distally. We used intramuscular recordings to measure the territory of muscle fibres from MG MUs and determine whether these MUs are grouped by recruitment threshold or joint action (ankle plantar flexion and knee flexion). The territory of MUs from the MG muscle varied from somewhat localized to highly distributed, with approximately half the MUs spanning at least half the length and width of the muscle. There was also no evidence of regional muscle activity based on MU recruitment thresholds or joint action. The CNS does not have the means to selectively activate regions of the MG muscle based on task requirements.

ABSTRACT

Human medial gastrocnemius (MG) motor units (MUs) are thought to occupy small muscle territories, with low-threshold units preferentially located distally. In this study, subjects (n = 8) performed ramped and sustained isometric contractions (ankle plantar flexion and knee flexion; range: ∼1-40% maximal voluntary contraction) and we measured MU territory size with spike-triggered averages from fine-wire electrodes inserted along the length (seven electrodes) or across the width (five electrodes) of the MG muscle. Of 69 MUs identified along the length of the muscle, 32 spanned at least half the muscle length (≥ 6.9 cm), 11 of which spanned all recording sites (13.6-17.9 cm). Distal fibres had smaller pennation angles (P < 0.05), which were accompanied by larger territories in MUs with fibres located distally (P < 0.05). There was no distal-to-proximal pattern of muscle activation in ramp contraction (P = 0.93). Of 36 MUs identified across the width of the muscle, 24 spanned at least half the muscle width (≥ 4.0 cm), 13 of which spanned all recording sites (8.0-10.8 cm). MUs were not localized (length or width) based on recruitment threshold or contraction type, nor was there a relationship between MU territory size and recruitment threshold (Spearman's rho = -0.20 and 0.13, P > 0.18). MUs in the human MG have larger territories than previously reported and are not localized based on recruitment threshold or joint action. This indicates that the CNS does not have the means to selectively activate regions of the MG muscle based on task requirements.

Motor Unit Characteristics after Targeted Muscle Reinnervation

Targeted muscle reinnervation (TMR) is a surgical procedure used to redirect nerves originally controlling muscles of the amputated limb into remaining muscles above the amputation, to treat phantom limb pain and facilitate prosthetic control. While this procedure effectively establishes robust prosthetic control, there is little knowledge on the behavior and characteristics of the reinnervated motor units. In this study we compared the m. pectoralis of five TMR patients to nine able-bodied controls with respect to motor unit action potential (MUAP) characteristics. We recorded and decomposed high-density surface EMG signals into individual spike trains of motor unit action potentials. In the TMR patients the MUAP surface area normalized to the electrode grid surface (0.25 ± 0.17 and 0.81 ± 0.46, p < 0.001) and the MUAP duration (10.92 ± 3.89 ms and 14.03 ± 3.91 ms, p < 0.01) were smaller for the TMR group than for the controls. The mean MUAP amplitude (0.19 ± 0.11 mV and 0.14 ± 0.06 mV, p = 0.07) was not significantly different between the two groups. Finally, we observed that MUAP surface representation in TMR generally overlapped, and the surface occupied by motor units corresponding to only one motor task was on average smaller than 12% of the electrode surface. These results suggest that smaller MUAP surface areas in TMR patients do not necessarily facilitate prosthetic control due to a high degree of overlap between these areas, and a neural information—based control could lead to improved performance. Based on the results we also infer that the size of the motor units after reinnervation is influenced by the size of the innervating motor neuron.

Effect of number of motor units and muscle fibre type on surface electromyogram

Reduction in number of motor units (nMU) and fast fibre ratio (FFR) is associated with disease or atrophy when this is rapid. There is a need to study the effect of nMU and FFR to analyse the association with ageing and disease. This study has developed a mathematical model to investigate the relationship between nMU and FFR on surface electromyogram (sEMG) of the biceps muscles. The model has been validated by comparing the simulation outcomes with experiments comparing the sEMG of physically active younger and older cohort. The results show that there is statistically significant difference between the two groups, and the simulation studies closely model the experimental results. This model can be applied to identify the cause of muscle weakness among the elderly due to factors such as muscle dystrophy or preferential loss of type F muscle fibres.

Towards identification of finger flexions using single channel surface electromyography--able bodied and amputee subjects

Background

This research has established a method for using single channel surface electromyogram (sEMG) recorded from the forearm to identify individual finger flexion. The technique uses the volume conduction properties of the tissues and uses the magnitude and density of the singularities in the signal as a measure of strength of the muscle activity.

Methods

SEMG was recorded from the flexor digitorum superficialis muscle during four different finger flexions. Based on the volume conduction properties of the tissues, sEMG was decomposed into wavelet maxima and grouped into four groups based on their magnitude. The mean magnitude and the density of each group were the inputs to the twin support vector machines (TSVM). The algorithm was tested on 11 able-bodied and one trans-radial amputated volunteer to determine the accuracy, sensitivity and specificity. The system was also tested to determine inter-experimental variations and variations due to difference in the electrode location.

Results

Accuracy and sensitivity of identification of finger actions from single channel sEMG signal was 93% and 94% for able-bodied and 81% and 84% for trans-radial amputated respectively, and there was only a small inter-experimental variation.

Conclusions

Volume conduction properties based sEMG analysis provides a suitable basis for identifying finger flexions from single channel sEMG. The reported system requires supervised training and automatic classification.

Volume conductor models in surface electromyography: computational techniques

Models of surface electromyogram (EMG) are useful to assess the effect of geometrical or conductivity properties of the tissue on the recorded signal. This paper provides a review of structure based models describing specific volume conductors. The technique for the development of advanced analytical and numerical simulators is described. A new model is also introduced, simulating a layered volume conductor including a subcutaneous tissue with variable thicknesses, providing an approximate analytical solution in the Fourier transform domain. Note that volume conductors are described using Poisson equation, fundamental model of Mathematical Physics, which applies also to mechanics, diffusion, electrostatics problems.

Frequency tuning of the cervical vestibular-evoked myogenic potential (cVEMP) recorded from multiple sites along the sternocleidomastoid muscle in normal human subjects

Frequency tuning of tone burst-evoked myogenic potentials recorded from the sternocleidomastoid muscle (cervical VEMP or cVEMP) is used clinically to assess vestibular function. Understanding the characteristics of cVEMP is important for improving the specificity of cVEMP testing in diagnosing vestibular deficits. In the present study, we analyzed the frequency tuning properties of the cVEMPs by constructing detailed tuning curves and examining their morphology and dependence on SCM tonic level, sound intensity, and recording site along the SCM. Here we report two main findings. First, by employing nine tone frequencies between 125 and 4,000 Hz, some tuning curves exhibited two distinct peaks, which cannot be modeled by a single mass spring system as previously suggested. Instead, the observed tuning is better modeled as linear summation of two mass spring systems, with resonance frequencies at ~300 and ~1,000 Hz. Peak frequency of cVEMP tuning curves was not affected by SCM tonic level, sound intensity, and location of recording site on the SCM. However, sharpness of cVEMP tuning was increased at lower sound intensities. Second, polarity of cVEMP responses recorded from the lower quarter of the SCM was reversed as compared to that at the two upper sites. While more studies are needed, these results suggest that cVEMP tuning is mediated through multiple generators with different resonance frequencies. Future studies are needed to explore implications of these results on development of selective VEMP tests and determine the nature of polarity inversion at the lower quarter of SCM.

Surface EMG model of the bicep during aging: a preliminary study

Reduction in the median frequency and the amplitude of surface electromyogram (sEMG) has been observed among older subjects compared with the younger cohort. These changes in sEMG have been associated with a reduction in the number of muscle fibers and a drop in the ratio of type II muscle fibers. However, the details of this association are not known. This paper has experimentally determined the difference between the magnitude and spectrum of sEMG of the younger and older cohorts, and estimated the changes to the muscle by populating a lifelike model with the experimental data. Experiments were conducted on subjects belonging to younger (20-28 years) and older (61-69) age groups. From the simulated results, it is shown that experimental sEMG signals are matched by the model representing the older cohort with a substantially reduced number of motor units compared to the younger people. In the model, the best match with experimental results was observed when the ratio of the bicep motor units between the older and the younger subjects was 0.5. The results also indicate a substantial reduction in the ratio of fast fibers, from 0.45 in the younger cohort to 0.11 in the older cohort.

Insights gained into the interpretation of surface electromyograms from the gastrocnemius muscles: A simulation study

Interpretation of surface electromyograms (EMG) is usually based on the assumption that the surface representation of action potentials does not change during their propagation. This assumption does not hold for muscles whose fibers are oblique to the skin. Consequently, the interpretation of surface EMGs recorded from pinnate muscles unlikely prompts from current knowledge. Here we present a complete analytical model that supports the interpretation of experimental EMGs detected from muscles with oblique architecture. EMGs were recorded from the medial gastrocnemius muscle during voluntary and electrically elicited contractions. Preliminary indications obtained from simulated and experimental signals concern the spatial localization of surface potentials and the myoelectric fatigue. Specifically, the spatial distribution of surface EMGs was localized about the fibers superficial extremity. Strikingly, this localization increased with the pinnation angle, both for the simulated EMGs and the recorded M-waves. Moreover, the average rectified value (ARV) and the mean frequency (MNF) of interference EMGs increased and decreased with simulated fatigue, respectively. The degree of variation in ARV and MNF did not depend on the pinnation angle simulated. Similar variations were observed for the experimental EMGs, although being less evident for a higher fiber inclination. These results are discussed on a physiological context, highlighting the relevance of the model proposed here for the interpretation of gastrocnemius EMGs and for conceiving future experiments on muscles with pinnate geometry.

A sEMG model with experimentally based simulation parameters

A differential, time-invariant, surface electromyogram (sEMG) model has been implemented. While it is based on existing EMG models, the novelty of this implementation is that it assigns more accurate distributions of variables to create realistic motor unit (MU) characteristics. Variables such as muscle fibre conduction velocity, jitter (the change in the interpulse interval between subsequent action potential firings) and motor unit size have been considered to follow normal distributions about an experimentally obtained mean. In addition, motor unit firing frequencies have been considered to have non-linear and type based distributions that are in accordance with experimental results. Motor unit recruitment thresholds have been considered to be related to the MU type. The model has been used to simulate single channel differential sEMG signals from voluntary, isometric contractions of the biceps brachii muscle. The model has been experimentally verified by conducting experiments on three subjects. Comparison between simulated signals and experimental recordings shows that the Root Mean Square (RMS) increases linearly with force in both cases. The simulated signals also show similar values and rates of change of RMS to the experimental signals.

Methodological aspects of SEMG recordings for force estimation--a tutorial and review

Insight into the magnitude of muscle forces is important in biomechanics research, for example because muscle forces are the main determinants of joint loading. Unfortunately muscle forces cannot be calculated directly and can only be measured using invasive procedures. Therefore, estimates of muscle force based on surface EMG measurements are frequently used. This review discusses the problems associated with surface EMG in muscle force estimation and the solutions that novel methodological developments provide to this problem. First, some basic aspects of muscle activity and EMG are reviewed and related to EMG amplitude estimation. The main methodological issues in EMG amplitude estimation are precision and representativeness. Lack of precision arises directly from the stochastic nature of the EMG signal as the summation of a series of randomly occurring polyphasic motor unit potentials and the resulting random constructive and destructive (phase cancellation) superimpositions. Representativeness is an issue due the structural and functional heterogeneity of muscles. Novel methods, i.e. multi-channel monopolar EMG and high-pass filtering or whitening of conventional bipolar EMG allow substantially less variable estimates of the EMG amplitude and yield better estimates of muscle force by (1) reducing effects of phase cancellation, and (2) adequate representation of the heterogeneous activity of motor units within a muscle. With such methods, highly accurate predictions of force, even of the minute force fluctuations that occur during an isometric and isotonic contraction have been achieved. For dynamic contractions, EMG-based force estimates are confounded by the effects of muscle length and contraction velocity on force producing capacity. These contractions require EMG amplitude estimates to be combined with modeling of muscle contraction dynamics to achieve valid force predictions.

Muscle fluid shift does not alter EMG global variables during sustained isometric actions

Body fluid redistribution occurs in astronauts traveling in space, potentially altering interstitial water content and hence impedance. This in turn may impact the features of electromyographic (EMG) signals measured to compare in-flight muscle function with pre- and post-flight conditions. Thus, the current study aimed at investigating the influence of similar fluid shifts on EMG spectral variables during muscle contractile activity. Ten men performed sustained isometric actions (120 s) at 20% and 60% of maximum voluntary contraction (MVC) following 1-h rest in the vertical or supine position. From single differential EMG signals, recorded from the soleus (SOL), the medial (MG) and lateral (LG) gastrocnemius muscles, initial value and rate of change over time (slope) of mean power frequency (MNF) and average rectified value (ARV) were assessed. MNF initial value showed dependence on muscle (P<0.01), but was unaffected by body tilt. MNF rate of change increased (P<0.001) with increased force and differed across muscles (P<0.05), but was not influenced (P=0.85) by altered body position. Thus, fluid shift resulting from vertical to supine tilt had no impact on myoelectrical manifestations of muscle fatigue. Furthermore, since such alteration of body fluid distribution resembles that occurring in microgravity, our findings suggest this may not be a methodological limitation, when comparing EMG fatigue indices on Earth versus in space.