Modeling of surface myoelectric signals--Part II: Model-based signal interpretation

Current developments in surface electromyography

Background/aim

Surface electromyography (surface EMG) is a primary technique to detect the electrical activities of muscles through surface electrodes. In recent years, surface EMG applications have grown from conventional fields into new fields. However, there is a gap between the progress in the research of surface EMG and its clinical acceptance, characterized by the translational knowledge and skills in the widespread use of surface EMG among the clinician community. To reduce this gap, it is necessary to translate the updated surface EMG applications and technological advances into clinical research. Therefore, we aimed to present a perspective on recent developments in the application of surface EMG and signal processing methods.

Materials and methods

We conducted this scoping review following the Joanna Briggs Institute (JBI) method. We conducted a general search of PubMed and Web of Science to identify key search terms. Following the search, we uploaded selected articles into Rayyan and removed duplicates. After prescreening 133 titles and abstracts, we assessed 91 full texts according to the inclusion criteria.

Results

We concluded that surface EMG has made innovative technological progress and has research potential for routine clinical applications and a wide range of applications, such as neurophysiology, sports and art performances, biofeedback, physical therapy and rehabilitation, assessment of physical exercises, muscle strength, fatigue, posture and postural control, movement analysis, muscle coordination, motor synergies, modelling, and more. Novel methods have been applied for surface EMG signals in terms of time domain, frequency domain, time-frequency domain, statistical methods, and nonlinear methods.

Conclusion

Translating innovations in surface EMG and signal analysis methods into routine clinical applications can be a helpful tool with a growing and valuable role in muscle activation measurement in clinical practices. Thus, researchers must build many more interfaces that give opportunities for continuing education and research with more contemporary techniques and devices.

Fundamental Concepts of Bipolar and High-Density Surface EMG Understanding and Teaching for Clinical, Occupational, and Sport Applications: Origin, Detection, and Main Errors

Surface electromyography (sEMG) has been the subject of thousands of scientific articles, but many barriers limit its clinical applications. Previous work has indicated that the lack of time, competence, training, and teaching is the main barrier to the clinical application of sEMG. This work follows up and presents a number of analogies, metaphors, and simulations using physical and mathematical models that provide tools for teaching sEMG detection by means of electrode pairs (1D signals) and electrode grids (2D and 3D signals). The basic mechanisms of sEMG generation are summarized and the features of the sensing system (electrode location, size, interelectrode distance, crosstalk, etc.) are illustrated (mostly by animations) with examples that teachers can use. The most common, as well as some potential, applications are illustrated in the areas of signal presentation, gait analysis, the optimal injection of botulinum toxin, neurorehabilitation, ergonomics, obstetrics, occupational medicine, and sport sciences. The work is primarily focused on correct sEMG detection and on crosstalk. Issues related to the clinical transfer of innovations are also discussed, as well as the need for training new clinical and/or technical operators in the field of sEMG.

Nonhomogeneous volume conduction effects affecting needle electromyography: an analytical and simulation study

Objective.Needle electromyography (EMG) is used to study the electrical behavior of myofiber properties in patients with neuromuscular disorders. However, due to the complexity of electrical potential spatial propagation in nonhomogeneous diseased muscle, a comprehensive understanding of volume conduction effects remains elusive. Here, we develop a framework to study the conduction effect of extracellular abnormalities and electrode positioning on extracellular local field potential (LFP) recordings.Methods.The framework describes the macroscopic conduction of electrical potential in an isotropic, nonhomogeneous (i.e. two tissue) model. Numerical and finite element model simulations are provided to study the conduction effect in prototypical monopolar EMG measurements.Results.LFPs recorded are influenced in amplitude, phase and duration by the electrode position in regards to the vicinity of tissue with different electrical properties.Conclusion.The framework reveals the influence of multiple mechanisms affecting LFPs including changes in the distance between the source-electrode and tissue electrical properties.Clinical significance.Our modeled predictions may lead to new ways for interpreting volume conduction effects on recorded EMG activity, for example in neuromuscular diseases that cause structural and compositional changes in muscle tissue. These change will manifest itself by changing the electric properties of the conductor media and will impact recorded potentials in the area of affected tissue.

Analytical Modelling of Surface EMG Signals Generated by Curvilinear Fibers with Approximate Conductivity Tensor

OBJECTIVE

Mathematical modelling of surface electromyographic (EMG) signals has been proven a valuable tool to interpret experimental data and to validate signal processing techniques. Most analytical EMG models only consider muscle fibers with specific and fixed arrangements. However, the fiber orientation and curvature may change along the fiber paths and may differ from fiber to fiber. Here we propose a subject-specific EMG model that simulates the fiber trajectories in muscles of the upper arm and analytically derives the action potentials assuming an approximate conductivity tensor.

METHODS

Magnetic Resonance (MR) images were acquired to identify and generate muscle fiber paths and to determine the muscle locations in a cylindrical volume conductor. While the propagation of the action potentials followed the identified curvilinear fiber paths, the conductivity tensor was not adapted to the fiber direction but approximated along the longitudinal axis of the cylindrical volume conductor. Single fiber action potentials (SFAPs) were computed by simulating the generation, propagation, and extinction of membrane current sources. To validate the assumption of the approximate conductivity tensor, two numerical models were implemented for comparison with the analytical solution. The first numerical model reproduced the analytical model and therefore included an approximation for the conductivity tensor. The second numerical model included the exact conductivity tensor derived from the fiber curvatures.

RESULTS

The motor unit action potentials generated by the proposed analytical model and the two numerical models were highly similar (cross-correlation >0.98, normalized root mean square error, nRMSE 0.04, relative error in the median frequency of the simulated waveforms of approximately 3%). The proposed analytical model was also evaluated by comparing simulated and experimentally recorded compound muscle action potentials (CMAPs). The CMAPs simulated with the proposed model better matched the experimental data (cross-correlation >0.90 and nRMSE <0.25 for the majority of the channels) than a model with straight fibers. Finally, the proposed model was representatively used to test the accuracy of an EMG decomposition algorithm, providing a realistic benchmark.

CONCLUSIONS AND SIGNIFICANCE

The proposed analytical model generates action potentials that reflect the spatial distributions of muscle fibers with curvilinear paths. The simulated signals are more realistic than signals generated by analytical models with straight fibers and can therefore be applied for testing EMG processing algorithms with a trade-off between simulation accuracy and computational speed.

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.

SVM-Based Classification of sEMG Signals for Upper-Limb Self-Rehabilitation Training

Robot-assisted rehabilitation is a growing field that can provide an intensity, quality, and quantity of treatment that exceed therapist-mediated rehabilitation. Several control algorithms have been implemented in rehabilitation robots to develop a patient-cooperative strategy with the capacity to understand the intention of the user and provide suitable rehabilitation training. In this paper, we present an upper-limb motion pattern recognition method using surface electromyography (sEMG) signals with a support vector machine (SVM) to control a rehabilitation robot, ReRobot, which was built to conduct upper-limb rehabilitation training for post-stroke patients. For poststroke rehabilitation training using the ReRobot, the upper-limb motion of the patient's healthy side is first recognized by detecting and processing the sEMG signals; then, the ReRobot assists the impaired arm in conducting mirror rehabilitation therapy. To train and test the SVM model, five healthy subjects participated in the experiments and performed five standard upper-limb motions, including shoulder flexion, abduction, internal rotation, external rotation, and elbow joint flexion. Good accuracy was demonstrated in experimental results from the five healthy subjects. By recognizing the model motion of the healthy side, the rehabilitation robot can provide mirror therapy to the affected side. This method can be used as a control strategy of upper-limb rehabilitation robots for self-rehabilitation training with stroke patients.

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.

Sex-specific reliability and multidimensional stability of responses to tests assessing neuromuscular function

The objective of this study was to estimate sex-specific effects in the test-retest cross-reliability of peripheral and central changes in nonlinear and linear measures of a surface electromyography signal during a brief (5 second) and sustained (2minute) isometric maximal voluntary contraction, combined with superimposed electrical stimulation involving the ankle plantar flexors over five identical trials. In this study, we repeated the testing protocol used in our previous study of 10 women (age 20.9, SD=0.3 years) (Bernecke et al., 2015) in a group of 10 men (age 21.2, SD=0.4 years). Despite the central (sex effect; p<0.05, η_p²>0.71, SP>70%) and peripheral fatigability (sex effect; p<0.01, η_p²>0.8, SP>90%) during sustained isometric maximal voluntary contraction, and lower reliability for central activation ratio during brief (intraclass correlation coefficient [ICC]=0.95 for men and ICC=0.82 for women) and sustained maximal voluntary contraction (ICC>0.82 for men and ICC>0.66 for women) over ankle plantar flexors expressed in women more than in men, all the ICCs of all indices measured by tests assessing neuromuscular function across the five identical test-retest trials were found as meaningful (correlation significance of p<0.05 was reached) and no significant differences were found between trials for any of the measured variables. In conclusion, the present study demonstrated greater central and peripheral fatigue for female participants following sustained (2minute) isometric maximal voluntary contraction of the plantar flexor muscles for all repeated trials and indicated an acceptable agreement between measurements of the characteristic variables made using the three different devices (dynamometry, electrical stimulation, and surface electromyography) over time for both sexes.

Wearable bio signal monitoring system applied to aviation safety

Pilots are required to have the ability to evaluate their own physical and psychological status to operate high performance aircrafts effectively. Existing studies have lacked consideration of applying bio signal of pilots in real time flight situation. The purpose of this study is to develop a wearable bio signal monitoring system that can measure the condition of pilots under an extreme flight environment to ensure flight safety. The wearable bio signal monitoring system consists of an algorithm for evaluating pilots' physiological stability, algorithms for detecting Gravity-induced Loss of Consciousness (G-LOC) prognosis, pilots' interaction module, and pilots' context awareness platform. The algorithm for evaluating pilots' physiological stability uses psychomotor cognitive test (PCT) and heart rate variability (HRV) to measure pilots' mission performance before flight. The algorithms for detecting G-LOC prognosis utilizes electromyogram (EMG) to generate warning signal during flight. The pilots' interaction module was developed for pilots to operate the system efficiently under flight environment. The pilots' context awareness platform was designed for the system to process multiple sensor signals in real time. This wearable bio signal monitoring system is expected to enhance flight safety and mission performance of pilots.

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.

Age-Associated Changes in the Spectral and Statistical Parameters of Surface Electromyogram of Tibialis Anterior

Age-related neuromuscular change of Tibialis Anterior (TA) is a leading cause of muscle strength decline among the elderly. This study has established the baseline for age-associated changes in sEMG of TA at different levels of voluntary contraction. We have investigated the use of Gaussianity and maximal power of the power spectral density (PSD) as suitable features to identify age-associated changes in the surface electromyogram (sEMG). Eighteen younger (20–30 years) and 18 older (60–85 years) cohorts completed two trials of isometric dorsiflexion at four different force levels between 10% and 50% of the maximal voluntary contraction. Gaussianity and maximal power of the PSD of sEMG were determined. Results show a significant increase in sEMG's maximal power of the PSD and Gaussianity with increase in force for both cohorts. It was also observed that older cohorts had higher maximal power of the PSD and lower Gaussianity. These age-related differences observed in the PSD and Gaussianity could be due to motor unit remodelling. This can be useful for noninvasive tracking of age-associated neuromuscular changes.

Age related neuromuscular changes in sEMG of m. Tibialis Anterior using higher order statistics (Gaussianity & linearity test)

Age-associated changes in the surface electromyogram (sEMG) of Tibialis Anterior (TA) muscle can be attributable to neuromuscular alterations that precede strength loss. We have used our sEMG model of the Tibialis Anterior to interpret the age-related changes and compared with the experimental sEMG. Eighteen young (20-30 years) and 18 older (60-85 years) performed isometric dorsiflexion at 6 different percentage levels of maximum voluntary contractions (MVC), and their sEMG from the TA muscle was recorded. Six different age-related changes in the neuromuscular system were simulated using the sEMG model at the same MVCs as the experiment. The maximal power of the spectrum, Gaussianity and Linearity Test Statistics were computed from the simulated and experimental sEMG. A correlation analysis at α=0.05 was performed between the simulated and experimental age-related change in the sEMG features. The results show the loss in motor units was distinguished by the Gaussianity and Linearity test statistics; while the maximal power of the PSD distinguished between the muscular factors. The simulated condition of 40% loss of motor units with halved the number of fast fibers best correlated with the age-related change observed in the experimental sEMG higher order statistical features. The simulated aging condition found by this study corresponds with the moderate motor unit remodelling and negligible strength loss reported in literature for the cohorts aged 60-70 years.

A Finite Element Model Approach to Determine the Influence of Electrode Design and Muscle Architecture on Myoelectric Signal Properties

Introduction

Surface electromyography (sEMG) is the measurement of the electrical activity of the skeletal muscle tissue detected at the skin’s surface. Typically, a bipolar electrode configuration is used. Most muscles have pennate and/or curved fibres, meaning it is not always feasible to align the bipolar electrodes along the fibres direction. Hence, there is a need to explore how different electrode designs can affect sEMG measurements.

Method

A three layer finite element (skin, fat, muscle) muscle model was used to explore different electrode designs. The implemented model used as source signal an experimentally recorded intramuscular EMG taken from the biceps brachii muscle of one healthy male. A wavelet based intensity analysis of the simulated sEMG signal was performed to analyze the power of the signal in the time and frequency domain.

Results

The model showed muscle tissue causing a bandwidth reduction (to 20-92- Hz). The inter-electrode distance (IED) and the electrode orientation relative to the fibres affected the total power but not the frequency filtering response. The effect of significant misalignment between the electrodes and the fibres (60°- 90°) could be reduced by increasing the IED (25–30 mm), which attenuates signal cancellation. When modelling pennated fibres, the muscle tissue started to act as a low pass filter. The effect of different IED seems to be enhanced in the pennated model, while the filtering response is changed considerably only when the electrodes are close to the signal termination within the model. For pennation angle greater than 20°, more than 50% of the source signal was attenuated, which can be compensated by increasing the IED to 25 mm.

Conclusion

Differences in tissue filtering properties, shown in our model, indicates that different electrode designs should be considered for muscle with different geometric properties (i.e. pennated muscles).

Detection of G-Induced Loss of Consciousness (G-LOC) prognosis through EMG monitoring on gastrocnemius muscle in flight

G-Induced Loss of Consciousness (G-LOC) is mainly caused by the sudden acceleration in the direction of +Gz axis from the fighter pilots, and is considered as an emergent situation of which fighter pilots are constantly aware. In order to resist against G-LOC, fighter pilots are subject to run Anti-G straining maneuver (AGSM), which includes L-1 respiration maneuvering and muscular contraction of the whole body. The purpose of this study is to create a G-LOC warning alarm prior to G-LOC by monitoring the Electromyogram (EMG) of the gastrocnemius muscle on the calf, which goes under constant muscular contraction during the AGSM process. EMG data was retrieved from pilots and pilot trainees of the Korean Air Force, during when subjects were under high G-trainings on a human centrifugal simulator. Out of the EMG features, integrated absolute value (IAV), reflecting muscle contraction, and waveform length (WL), reflecting muscle contraction and fatigue, have shown a rapid decay during the alarm phase, 3 seconds before G-LOC, compared to that of a normal phase withstanding G-force. Such results showed consistency amongst pilots and pilot trainees who were under G-LOC. Based on these findings, this study developed an algorithm which can detect G-LOC prognosis during flight, and at the same time, generate warning signals. The probability of G-LOC occurrence is detected through monitoring the decay trend and degree of the IVA and WL value of when the pilot initiates AGSM during sudden acceleration above 6G. Conclusively, this G-LOC prognosis detecting and warning system is a customized, real-time countermeasure which enhanced the accuracy of detecting G-LOC.

Spatial variation of compound muscle action potentials across human gastrocnemius medialis

The massed action potential (M wave) elicited through nerve stimulation underpins a wide range of physiological and mechanical understanding of skeletal muscle structure and function. Although systematic approaches have evaluated the effect of different factors on M waves, the effect of the location and distribution of activated fibers within the muscle remains unknown. By detecting M waves from the medial gastrocnemius (MG) of 12 participants with a grid of 128 electrodes, we investigated whether different populations of muscle units have different spatial organization within MG. If populations of muscle units occupy discrete MG regions, current pulses of progressively greater intensities applied to the MG nerve branch would be expected to lead to local changes in M-wave amplitudes. Electrical pulses were therefore delivered at 2 pps, with the current pulse amplitude increased every 10 stimuli to elicit different degrees of muscle activation. The localization of MG response to increases in current intensity was determined from the spatial distribution of M-wave amplitude. Key results revealed that increases in M-wave amplitude were detected somewhat locally, by 10-50% of the 128 electrodes. Most importantly, the electrodes detecting greatest increases in M-wave amplitude were localized at different regions in the grid, with a tendency for greater stimulation intensities to elicit M waves in the more distal MG region. The presented results indicate that M waves recorded locally may not provide a representative MG response, with major implications for the estimation of, e.g., the maximal stimulation levels, the number of motor units, and the onset and normalization in H-reflex studies.

Spatial variability of muscle activity during human walking: the effects of different EMG normalization approaches

Human leg muscles are often activated inhomogeneously, e.g. in standing. This may also occur in complex tasks like walking. Thus, bipolar surface electromyography (sEMG) may not accurately represent whole muscle activity. This study used 64-electrode high-density sEMG (HD-sEMG) to examine spatial variability of lateral gastrocnemius (LG) muscle activity during the stance phase of walking, maximal voluntary contractions (MVCs) and maximal M-waves, and determined the effects of different normalization approaches on spatial and inter-participant variability. Plantar flexion MVC, maximal electrically elicited M-waves and walking at self-selected speed were recorded in eight healthy males aged 24-34. sEMG signals were assessed in four ways: unnormalized, and normalized to MVC, M-wave or peak sEMG during the stance phase of walking. During walking, LG activity varied spatially, and was largest in the distal and lateral regions. Spatial variability fluctuated throughout the stance phase. Normalizing walking EMG signals to the peak value during stance reduced spatial variability within LG on average by 70%, and inter-participant variability by 67%. Normalizing to MVC reduced spatial variability by 17% but increased inter-participant variability by 230%. Normalizing to M-wave produced the greatest spatial variability (45% greater than unnormalized EMG) and increased inter-participant variability by 70%. Unnormalized bipolar LG sEMG may provide misleading results about representative muscle activity in walking due to spatial variability. For the peak value and MVC approaches, different electrode locations likely have minor effects on normalized results, whereas electrode location should be carefully considered when normalizing walking sEMG data to maximal M-waves.

A novel spatiotemporal muscle activity imaging approach based on the Extended Kalman Filter

A novel spatiotemporal muscle activity imaging (sMAI) approach has been developed using the Extended Kalman Filter (EKF) to reconstruct internal muscle activities from non-invasive multi-channel surface electromyogram (sEMG) recordings. A distributed bioelectric dipole source model is employed to describe the internal muscle activity space, and a linear relationship between the muscle activity space and the sEMG measurement space is then established. The EKF is employed to recursively solve the ill-posed inverse problem in the sMAI approach, in which the weighted minimum norm (WMN) method is utilized to calculate the initial state and a new nonlinear method is developed based on the propagating features of muscle activities to predict the recursive state. A series of computer simulations was conducted to test the performance of the proposed sMAI approach. Results show that the localization error rapidly decreases over 35% and the overlap ratio rapidly increases over 45% compared to the results achieved using the WMN method only. The present promising results demonstrate the feasibility of utilizing the proposed EKF-based sMAI approach to accurately reconstruct internal muscle activities from non-invasive sEMG recordings.

Do surface electromyograms provide physiological estimates of conduction velocity from the medial gastrocnemius muscle?

Muscle fiber conduction velocity (CV) is commonly estimated from surface electromyograms (EMGs) collected with electrodes parallel to muscle fibers. If electrodes and muscle fibers are not located in parallel planes, CV estimates are biased towards values far over the physiological range. In virtue of their pinnate architecture, the fibers of muscles such as the gastrocnemius are hardly aligned in planes parallel to surface electrodes. Therefore, in this study we investigate whether physiological CV estimates can be obtained from the gastrocnemius muscle. Specifically, with a large grid of 16×8 electrodes we map CV estimates over the whole gastrocnemius muscle while eleven subjects exerted isometric plantar flexions at three different force levels. CV was estimated for couples of single differential EMGs and estimate locations (i.e., channels) were classified as physiological and non-physiological, depending on whether CV estimates were within the physiological range (3-6ms(-1)) or not. Physiological CV values could be estimated from a markedly small muscle region for eight participants; channels providing physiological CV estimates corresponded to about 5% of the total number of channels. As expected, physiological and non-physiological channels were clustered in distinct regions. CV estimates within the physiological range were obtained for the most distal gastrocnemius portion (ANOVA, P<0.001), where occurrences of propagating potentials were often verified through visual analysis. For the first time, this study shows that CV might be reliably assessed from surface EMGs collected from the most distal gastrocnemius region.

A simulation study on the relation between muscle motor unit numbers and the non-Gaussianity/non-linearity levels of surface electromyography

Recent research has demonstrated that surface electromyography (sEMG) signals have non-Gaussianity and non-linearity properties. It is known that more muscle motor units are recruited and firing rates (FRs) increase as exertion increases. A hypothesis was proposed that the Gaussianity test (S (g)) and linearity test (S (ℓ)) levels of sEMG signals are associated with the number of active motor units (nMUs) and the FR. The hypothesis has only been preliminarily discussed in experimental studies. We used a simulation sEMG model involving spatial (active MUs) and temporal (three FRs) information to test the hypothesis. Higher-order statistics (HOS) from the bi-frequency domain were used to perform S (g) and S (ℓ). Multivariate covariance analysis and a correlation test were employed to determine the nMUs-S (g) relationship and the nMUs-S (ℓ) relationship. Results showed that nMUs, the FR, and the interaction of nMUs and the FR all influenced the S (g) and S (ℓ) values. The nMUs negatively correlated to both the S (g) and S (ℓ) values. That is, at the three FRs, sEMG signals tended to a more Gaussian and linear distribution as exertion and nMUs increased. The study limited experiment factors to the sEMG non-Gaussianity and non-linearity levels. The study quantitatively described nMUs and the FR of muscle that are not directly available from experiments. Our finding has guiding significance for muscle capability assessment and prosthetic control.

A note on the probability distribution function of the surface electromyogram signal

The probability density function (PDF) of the surface electromyogram (EMG) signals has been modelled with Gaussian and Laplacian distribution functions. However, a general consensus upon the PDF of the EMG signals is yet to be reached, because not only are there several biological factors that can influence this distribution function, but also different analysis techniques can lead to contradicting results. Here, we recorded the EMG signal at different isometric muscle contraction levels and characterised the probability distribution of the surface EMG signal with two statistical measures: bicoherence and kurtosis. Bicoherence analysis did not help to infer the PDF of measured EMG signals. In contrast, with kurtosis analysis we demonstrated that the EMG PDF at isometric, non-fatiguing, low contraction levels is super-Gaussian. Moreover, kurtosis analysis showed that as the contraction force increases the surface EMG PDF tends to a Gaussian distribution.

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.

Mapping the motor point in the human tibialis anterior muscle

OBJECTIVE

Percutaneous electrical stimulation of the motor point permits selective activation of a muscle. However, the definition and number of motor points reported for a given muscle varies. Our goal was to address these problems.

METHODS

The area, location and number of motor points in human tibialis anterior were examined, using isometric dorsiflexion torque responses to electrical stimuli. Three methods were used: lowest electrical threshold, maximum muscle response, and approximate motor point.

RESULTS

A single motor point was identified in 39/40 subjects regardless of method. The area of the site of lowest electrical threshold was smaller (median, 35 mm(2)) than those using the maximum muscle response (144 mm(2)) and approximate motor point (132 mm(2)). There was substantial, but not significant, between-subject variation in motor point location. Fifty three percent of motor points would have been missed if located only by reference to anatomical landmarks.

CONCLUSIONS

These results suggested that the motor point's location cannot be determined a priori and that the identification method will affect both area and location.

SIGNIFICANCE

If it is important to maximally activate a single muscle in isolation, the motor point is best represented by the site producing a maximal but isolated muscle response at the lowest stimulation intensity.

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.

A muscle architecture model offering control over motor unit fiber density distributions

The aim of this study was to develop a muscle architecture model able to account for the observed distributions of innervation ratios and fiber densities of different types of motor units in a muscle. A model algorithm is proposed and mathematically analyzed in order to obtain an inverse procedure that allows, by modification of input parameters, control over the output distributions of motor unit fiber densities. The model's performance was tested with independent data from a glycogen depletion study of the medial gastrocnemius of the rat. Results show that the model accurately reproduces the observed physiological distributions of innervation ratios and fiber densities and their relationships. The reliability and accuracy of the new muscle architecture model developed here can provide more accurate models for the simulation of different electromyographic signals.

Is the stabilization of quiet upright stance in humans driven by synchronized modulations of the activity of medial and lateral gastrocnemius muscles?

A matrix of 120 electromyogram (EMG) electrodes (8 rows and 15 columns) was used to investigate individual activation patterns of the medial (MG) and lateral gastrocnemius (LG) muscles during forward sways of the body in human quiet stance. This matrix was positioned on the right calf of eight subjects after identification of the MG and LG contours with ultrasound scanning. Gray-scale images were generated with the maxima and minima of the cross-correlation function between the envelope of each EMG signal and the body center of pressure (CoP) for individual forward sways. These images were automatically segmented to reduce the data set into representative and local values of EMG-CoP cross-correlation for each muscle. On average, modulations in EMG amplitude preceded the onset of forward sways with a variable timing, with both gastrocnemius muscles being similarly and synchronously modulated in 193 out of 236 sways. Variations in the timing of activation between muscles were less frequent, although consistent across subjects and significantly correlated with changes in the direction and velocity of body sways. Interestingly, the time shift between EMG and CoP traces sometimes varied consistently along different channels of the same column of electrodes, either in proximal-to-distal or distal-to-proximal direction. The variable EMG-CoP cross-correlation delay was not congruent with the delay expected for the propagation of surface potentials along muscle fibers. Comparison of surface EMGs with intramuscular EMGs recorded from six subjects demonstrated that surface potentials provide high spatial selectivity, thus supporting the notion of selective activation of motor units during quiet standing. Hence, the stabilization of the quiet standing posture likely relies on flexible rather than stereotyped mechanisms of control.

A statistical model and simulator for cardiovascular pressure signals

Physiological signal simulators are often used to conduct validation studies of commercially available devices such as oscillometric non-invasive blood pressure (NIBP) monitors. Numerous assessment studies have been conducted using simulators to validate commercial NIBP monitors. While there are several simulators commercially available to evaluate oscillometric NIBP devices, currently there are no simulators designed to validate invasive pressure signal devices. A statistical model and simulator for invasive cardiovascular pressure signals such as arterial blood pressure and intracranial pressure are described. The model incorporates the effects of respiration on pressure signals and can be used to generate synthetic signals with time and frequency domain characteristics matching any desired subject population. Additionally, the way that noise and artefacts typically present in real pressure signals should be modelled is described. The proposed statistical model is a useful tool for validation of algorithms designed to process or analyse biomedical pressure signals to estimate parameters of clinical interest such as the cardiac frequency, heart rate variability, respiratory frequency, and pulse pressure variation in the presence of noise. The model can be used to simulate signals in order to validate commercial devices that process and analyse invasive pressure signals.

A simulation study for a surface EMG sensor that detects distinguishable motor unit action potentials

An advanced volume conductor model was used to simulate the surface-detected motor unit action potentials (MUAPs) due to current sources located at different depths within the muscle tissue of the biceps brachii. Seven different spatial filters were investigated by linear summation of the monopolarly detected surface MUAPs on a square array of nine electrodes. The criterion of the relative energy-of-difference (EOD) between the MUAPs was used to rank spatial filters for their ability to distinguish two motor units located at different depths. Using the same criterion pair wise combinations of spatial filters were ranked for their ability to generate different MUAP shape representations of the same motor unit. In both analyses, the bi-transversal double-differential (BiTDD) configurations and pair wise combinations involving a BiTDD configuration consistently ranked highest. Varying electrode spacing did not change the results in a relevant way. Based on the EOD calculations, a four-channel detection system using all available electrodes of the array is proposed. The implications of using only six electrodes, effectively reducing contact area of the sensor in half, are discussed.

Simulation analysis of interference EMG during fatiguing voluntary contractions. Part I: What do the intramuscular spike amplitude–frequency histograms reflect?

Decline in amplitude of EMG signals and in the rate of counts of intramuscularly recorded spikes during fatigue is often attributed to a progressive reduction of the neural drive only. As a rule, alterations in intracellular action potential (IAP) are not taken into account. To test correctness of the hypothesis, the effect of various discharge frequency patterns as well as changes in IAP shape and muscle fibre propagation velocity (MFPV) on the spike amplitude-frequency histogram of intramuscular interference EMG signals were simulated and analyzed. It was assumed that muscle was composed of four types of motor units (MUs): slow-twitch fatigue resistant, fast-twitch fatigue resistant, fast intermediate, and fast fatigable. MFPV and IAP duration at initial stage before fatigue as well as their changes differed for individual MU types. Fatigability of individual MU types in normal conditions as well as in the case of ischaemic or low oxygen conditions due to restricted blood flow was also taken into account. It was found that spike amplitude-frequency histogram is poorly sensitive to MU firing frequency, while it is highly sensitive to IAP profile lengthening. It is concluded that spike amplitude-frequency analysis can hardly provide a correct measure of MU rate-coding pattern during fatigue.

Experimental and modeling investigation of spectral compression of biceps brachii SEMG activity with increasing force levels

This study investigated possible motor unit (MU) firing patterns underlying changes in biceps brachii (BB) surface electromyographic (SEMG) activity in 96 participants who performed isometric actions of the elbow flexors at 40%, 60%, 80%, and 100% of maximum voluntary contraction (MVC). We also conducted a modeling investigation to determine the extent to which a model would fit the experimental results. Experimentally, there was a linear increase (277%; p<0.01) in root-mean-square (RMS) amplitude with increasing force. The mean power frequency (MNF) remained stable from 40% to 80% of MVC, but there was a decrease (8.2%; p<0.01) between 80% and 100% of MVC. A modeling approach was taken wherein well-known recruitment and rate-coding schemes activated MUs whose basic building block was the muscle fibre action potential. Two conditions were investigated: (1) an increase in firing rate (rate-coding) and (2) synchronization. The levels of rate-coding and synchronization were selected to produce a linear RMS-force relationship as observed in the experimental data. Then, the impact of these two strategies on changes in MNF was assessed. The MNF remained stable from 40% to 80% of maximum excitation for both the rate-coding and synchronization conditions. There was a decrease in MNF between 80% and 100% of maximum excitation for both modeling conditions, similar to that observed for the experimental data. Thus, at these high forces at which experimental data are technically difficult to obtain, the model supports the idea that both rate-coding and synchronization are responsible for the changes observed in surface EMG amplitude and frequency characteristics.

Efficient parameterization of MUAP signal for identification of MU and volume conductor characteristics using neural networks

The motor unit action potential (MUAPs) shapes depend on the anatomy and the physiology of the contracted muscle. The aim of this work is the identification of some characteristics of the motor unit (MU) and the volume conductor, namely the MU depth, the innervation zone width and the thickness of fat and skin layers based on MUAP signal parameters. The relationship between these characteristics and MUAP parameters are non-linear and complex. Thus, the use of the neural networks approach becomes an efficient tool to put in evidence this relationship. We have used the similarity and the homogeneity of the parameter criterions to choose which parameters are appropriate for the extraction. Two identification systems are presented and compared, a global system and a separate one. In order to evaluate the performance of each system, we have tested them using several simulated MUAP signals corrupted with additive Gaussian noise at different signal to noise ratios (SNR). A new test is introduced in which the electrode radius, the bar electrode dimensions and inclination angles for the detection system, fixed during the training process, are changed.

Ankle position and voluntary contraction alter maximal M waves in soleus and tibialis anterior

Compound muscle action potentials (CMAPs) recorded using surface electrodes are often used to assess the excitability of neural pathways to skeletal muscle. However, the amplitude of CMAPs can be influenced by changes at the recording site, independent of mechanisms within the central nervous system. We quantified how joint angle and background contraction influenced CMAP amplitude. In seven subjects CMAPs evoked by supramaximal transcutaneous electrical stimulation of motor axons (M(max)) were recorded using surface electrodes from soleus and tibialis anterior (TA) at static positions over the full range of ankle movement at 5 degrees intervals. Across subjects the peak-to-peak amplitude of M(max) was 155% and 159% larger at the shortest than longest muscle lengths for soleus and TA, respectively. In five subjects the effect of ankle position and voluntary contraction on M-wave/H-reflex recruitment curves was assessed in the soleus. Both ankle position and level of contraction significantly influenced M(max), H(max), and the H(max) to M(max) ratio, but there were no interactions between the two parameters. These peripheral changes that influence M(max) will also impact other CMAPs such as submaximal M-waves, H-reflexes, and responses to transcranial magnetic stimulation. As such, during experimental studies CMAPs evoked at a given joint angle and contraction level should be normalized to M(max) recorded at similar joint angle and contraction strength.

Bayesian filtering of myoelectric signals

Surface electromyography is used in research, to estimate the activity of muscle, in prosthetic design, to provide a control signal, and in biofeedback, to provide subjects with a visual or auditory indication of muscle contraction. Unfortunately, successful applications are limited by the variability in the signal and the consequent poor quality of estimates. I propose to use a nonlinear recursive filter based on Bayesian estimation. The desired filtered signal is modeled as a combined diffusion and jump process and the measured electromyographic (EMG) signal is modeled as a random process with a density in the exponential family and rate given by the desired signal. The rate is estimated on-line by calculating the full conditional density given all past measurements from a single electrode. The Bayesian estimate gives the filtered signal that best describes the observed EMG signal. This estimate yields results with very low short-time variability but also with the capability of very rapid response to change. The estimate approximates isometric joint torque with lower error and higher signal-to-noise ratio than current linear methods. Use of the nonlinear filter significantly reduces noise compared with current algorithms, and it may therefore permit more effective use of the EMG signal for prosthetic control, biofeedback, and neurophysiology research.

Clinical applications of high-density surface EMG: A systematic review

High density-surface EMG (HD-sEMG) is a non-invasive technique to measure electrical muscle activity with multiple (more than two) closely spaced electrodes overlying a restricted area of the skin. Besides temporal activity HD-sEMG also allows spatial EMG activity to be recorded, thus expanding the possibilities to detect new muscle characteristics. Especially muscle fiber conduction velocity (MFCV) measurements and the evaluation of single motor unit (MU) characteristics come into view. This systematic review of the literature evaluates the clinical applications of HD-sEMG. Although beyond the scope of the present review, the search yielded a large number of "non-clinical" papers demonstrating that a considerable amount of work has been done and that significant technical progress has been made concerning the feasibility and optimization of HD-sEMG techniques. Twenty-nine clinical studies and four reviews of clinical applications of HD-sEMG were considered. The clinical studies concerned muscle fatigue, motor neuron diseases (MND), neuropathies, myopathies (mainly in patients with channelopathies), spontaneous muscle activity and MU firing rates. In principle, HD-sEMG allows pathological changes at the MU level to be detected, especially changes in neurogenic disorders and channelopathies. We additionally discuss several bioengineering aspects and future clinical applications of the technique and provide recommendations for further development and implementation of HD-sEMG as a clinical diagnostic tool.

Simulation of Surface EMG Signals for a Multilayer Volume Conductor With Triangular Model of the Muscle Tissue

This study analytically describes surface electromyogram (sEMG) signals generated by a model of a triangular muscle, i.e., a muscle with fibers arranged in a fan shape. Examples of triangular muscles in the human body are the deltoid, the pectoralis major, the trapezius, the adductor pollicis. A model of triangular muscle is proposed. It is a sector of a cylindrical volume conductor (with the fibers directed along the radial coordinate) bounded at the muscle/fat interface. The muscle conductivity tensor reflects the fan anisotropy. Edge effects have been neglected. A solution of the nonspace invariant problem for a triangular muscle is provided in the Fourier domain. An approximate analytical solution for a two plane layer volume conductor model is obtained by introducing a homogeneous layer (modeling the fat) over the triangular muscle. The results are implemented in a complete sEMG generation model (including the finite length of the fibers), simulating single fiber action potentials. The model is not space invariant due to the changes of the volume conductor along the direction of action potential propagation. Thus the detected potentials at the skin surface change shape as they propagate. This determines problems in the extraction and interpretation of parameters. As a representative example of application of the simulation model, the influence of the inhomogeneity of the volume conductor in conduction velocity (CV) estimation is addressed (for two channels; maximum likelihood and reference point methods). Different fiber depths, electrode placements and small misalignments of the detection system with respect to the fiber have been simulated. The error in CV estimation is large when the depth of the fiber increases, when the detection system is not aligned with the fiber and close to the innervation point and to the tendons.

Improving EMG-based muscle force estimation by using a high-density EMG grid and principal component analysis

The accuracy of predictions of muscle force based on electromyography (EMG) is an important issue in biomechanics and kinesiology. Since human skeletal muscles show a high diversity and heterogeneity in their fiber architecture, it is difficult to properly align electrodes to the muscle fiber direction. Against this background, we analyzed the effect of different bipolar configuration directions on EMG-based force estimation. In addition, we investigated whether principal component analysis (PCA) can improve this estimation. High-density surface-EMG from the triceps brachii muscle and the extension force of the elbow were measured in 11 subjects. The root mean square difference (RMSD) between predicted and measured force was determined. We found the best bipolar configuration direction to cause a 13% lower RMSD relative to the worst direction. Optimal results were obtained with electrodes aligned with the expected main muscle fiber direction. We found that PCA reduced RMSD by about 40% compared to conventional bipolar electrodes and by about 12% compared to optimally aligned multiple bipolar electrodes. Thus, PCA contributes to the accuracy of EMG-based estimation of muscle force when using a high-density EMG grid.

An analytical model for surface EMG generation in volume conductors with smooth conductivity variations

A nonspace invariant model of volume conductor for surface electromyography (EMG) signal generation is analytically investigated. The volume conductor comprises planar layers representing the muscle and subcutaneous tissues. The muscle tissue is homogeneous and anisotropic while the subcutaneous layer is inhomogeneous and isotropic. The inhomogeneity is modeled as a smooth variation in conductivity along the muscle fiber direction. This may reflect a practical situation of tissues with different conductivity properties in different locations or of transitions between tissues with different properties. The problem is studied with the regular perturbation theory, through a series expansion of the electric potential. This leads to a set of Poisson's problems, for which the source term in an equation and the boundary conditions are determined by the solution of the previous equations. This set of problems can be solved iteratively. The solution is obtained in the two-dimensional Fourier domain, with spatial angular frequencies corresponding to the longitudinal and perpendicular direction with respect to the muscle fibers, in planes parallel to the detection surface. The series expansion is truncated for the practical implementation. Representative simulations are presented. The proposed model constitutes a new approach for surface EMG signal simulation with applications related to the validation of methods for information extraction from this signal.

The mechanomyography of persons after stroke during isometric voluntary contractions

This study was to investigate the properties of mechanomyography (MMG), or muscle sound, of the paretic muscle in the affected side of hemiplegic subjects after stroke during isometric voluntary contractions, in comparison with those from the muscle in the unaffected side of the hemiplegic subjects and from the healthy muscle of unimpaired subjects. MMG and electromyography (EMG) signals were recorded simultaneously from the biceps brachii muscles of the dominant arm of unimpaired subjects (n=5) and the unaffected and affected arms of subjects after stroke (n=8), when performing a fatiguing maximal voluntary contraction (MVC) associated with the decrease in elbow flexion torque, and then submaximal elbow flexions at 20%, 40%, 60% and 80% MVCs. The root mean squared (RMS) values, the mean power frequencies (MPF, in the power density spectrum, PDS) of the EMG and MMG, and the high frequency rate (HF-rate, the ratio of the power above 15Hz in the MMG PDS) were used for the analysis. The MMG RMS decreased more slowly during the MVC in the affected muscle compared to the healthy and unaffected muscles. A transient increase could be observed in the MMG MPFs from the unaffected and healthy muscles during the MVC, associated with the decrease in their simultaneous EMG MPFs due to the muscular fatigue. No significant variation could be seen in the EMG and MMG MPFs in the affected muscles during the MVC. The values in the MPF and HF-rate of MMG from the affected muscles were significantly lower than those from the healthy and unaffected muscles (P<0.05) at the high contraction level (80% MVC). Both the MMG and EMG RMS values in the healthy and unaffected groups were found to be significantly higher than the affected group (P<0.05) at 60% and 80% MVCs. These observations were related to an atrophy of the fast-twitch fibers and a reduction of the neural input in the affected muscles of the hemiplegic subjects. The results in this study suggested MMG could be used as a complementary to EMG for the analysis on muscular characteristics in subjects after stroke.

A Finite Element Model for Describing the Effect of Muscle Shortening on Surface EMG

A finite-element model for the generation of single fiber action potentials in a muscle undergoing various degrees of fiber shortening is developed. The muscle is assumed fusiform with muscle fibers following a curvilinear path described by a Gaussian function. Different degrees of fiber shortening are simulated by changing the parameters of the fiber path and maintaining the volume of the muscle constant. The conductivity tensor is adapted to the muscle fiber orientation. In each point of the volume conductor, the conductivity of the muscle tissue in the direction of the fiber is larger than that in the transversal direction. Thus, the conductivity tensor changes point-by-point with fiber shortening, adapting to the fiber paths. An analytical derivation of the conductivity tensor is provided. The volume conductor is then studied with a finite-element approach using the analytically derived conductivity tensor. Representative simulations of single fiber action potentials with the muscle at different degrees of shortening are presented. It is shown that the geometrical changes in the muscle, which imply changes in the conductivity tensor, determine important variations in action potential shape, thus affecting its amplitude and frequency content. The model provides a new tool for interpreting surface EMG signal features with changes in muscle geometry, as it happens during dynamic contractions.