Volume Conduction in an Anatomically Based Surface EMG Model

Nonlinear spatio-temporal filter to reduce crosstalk in bipolar electromyogram

Objective.The wide detection volume of surface electromyogram (EMG) makes it prone to crosstalk, i.e. the signal from other muscles than the target one. Removing this perturbation from bipolar recordings is an important open problem for many applications.Approach.An innovative nonlinear spatio-temporal filter is developed to estimate the EMG generated by the target muscle by processing noisy signals from two bipolar channels, placed over the target and the crosstalk muscle, respectively. The filter is trained on some calibration data and then can be applied on new signals. Tests are provided in simulations (considering different thicknesses of the subcutaneous tissue, inter-electrode distances, locations of the EMG channels, force levels) and experiments (from pronator teres and flexor carpi radialis of 8 healthy subjects).Main results.The proposed filter allows to reduce the effect of crosstalk in all investigated conditions, with a statistically significant reduction of its root mean squared of about 20%, both in simulated and experimental data. Its performances are also superior to those of a blind source separation method applied to the same data.Significance.The proposed filter is simple to be applied and feasible in applications in which single bipolar channels are placed over the muscles of interest. It can be useful in many fields, such as in gait analysis, tests of myoelectric fatigue, rehabilitation with EMG biofeedback, clinical studies, prosthesis control.

Forearm muscle activity estimation based on anatomical structure of muscles

Estimation of muscle activity using surface electromyography (sEMG) is an important non-invasive method that can lead to a deeper understanding of motor-control strategies in humans. Measurement using multiple active electrodes is necessary to estimate not only surface muscle activity but also deep muscle activity in dynamic motion. In this paper, we propose a method for estimating muscle activity of dynamic motions based on anatomical knowledge of muscle structures. To estimate muscle activity, a large number of signal sources are set in the muscle model, and connections between the signal sources are defined a priori based on the anatomical structure of the muscles. The signal source activities are first estimated by minimizing the Kullback-Leibler divergence with a continuity cost. Then, the muscle activity is computed from the signal source activity. In the experiments, five healthy participants performed five types of motion and the forearm sEMG was measured with 20-channel active electrodes. The estimation results for these motions were visualized in four dimensions as the three-dimensional position of the muscle over time. The results showed that the estimation was accurate, with a reproduction rate of 95% for the measured sEMG and continuity of the muscle activity. In addition, the results suggest the advantage of the proposed method over the conventional approaches in terms of estimating the muscle activity for both dynamic and abnormal motions.

Endurance-exercise training adaptations in spinal motoneurones: potential functional relevance to locomotor output and assessment in humans

It is clear from non-human animal work that spinal motoneurones undergo endurance training (chronic) and locomotor (acute) related changes in their electrical properties and thus their ability to fire action potentials in response to synaptic input. The functional implications of these changes, however, are speculative. In humans, data suggests that similar chronic and acute changes in motoneurone excitability may occur, though the work is limited due to technical constraints. To examine the potential influence of chronic changes in human motoneurone excitability on the acute changes that occur during locomotor output, we must develop more sophisticated recording techniques or adapt our current methods. In this review, we briefly discuss chronic and acute changes in motoneurone excitability arising from non-human and human work. We then discuss the potential interaction effects of chronic and acute changes in motoneurone excitability and the potential impact on locomotor output. Finally, we discuss the use of high-density surface electromyogram recordings to examine human motor unit firing patterns and thus, indirectly, motoneurone excitability. The assessment of single motor units from high-density recording is mainly limited to tonic motor outputs and minimally dynamic motor output such as postural sway. Adapting this technology for use during locomotor outputs would allow us to gain a better understanding of the potential functional implications of endurance training-induced changes in human motoneurone excitability on motor output.

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.

Increased preferential activation of small cutaneous nerve fibers by optimization of electrode design parameters

OBJECTIVE

Electrical preferential activation of small nociceptive fibers may be achieved with the use of specialized small area electrodes, however, the existing electrodes are limited to low stimulation intensities. As existing electrodes have been developed empirically, the present study aimed to use computational modeling and optimization techniques to investigate if changes in electrode design parameters could improve the preferential activation of small fibers.

APPROACH

Two finite element models; one of a planar concentric and one of an intra-epidermal electrode were combined with two multi-compartmental nerve fiber models of an Aδ-fiber and an Aβ-fiber. These two-step hybrid models were used for the optimization of four electrode parameters; anode area, anode-cathode distance, cathode area, and cathode protrusion. Optimization was performed using a gradient-free bounded Nelder-Mead algorithm, to maximize the current activation threshold ratio between the Aβ-fiber model and the Aδ-fiber model.

MAIN RESULTS

All electrode parameters were optimal at their lower bound, except the cathode protrusion, which was optimal a few micrometers above the location of the Aδ-fiber model. A small cathode area is essential for producing a high current density in the epidermal skin layer enabling activation of small fibers, while a small anode area and anode-cathode distance are important for the minimization of the current spread to deeper tissues, making it less likely to activate large fibers. Combining each of the optimized electrode parameters improved the preferential activation of small fibers in comparison to existing electrodes, by increasing the activation threshold ratio between the two nerve fiber types. The maximum increase in the activation threshold ratio was 289% and 595% for the intra-epidermal and planar concentric design, respectively.

SIGNIFICANCE

The present study showed that electrical preferential small fiber activation can be improved by electrode design. Additionally, the results may be used for the production of an electrode that could potentially be used for clinical assessment of small fiber neuropathy.

Electromyographic amplitude versus torque relationships are different in young versus postmenopausal females and are related to muscle mass after controlling for bodyweight

PURPOSE

To examine differences in the electromyographic vs torque (EMG-T) relationship, as well as muscle strength and indicators of muscle mass and quality between young versus postmenopausal females, and explore whether the potential differences in the EMG-T relationships could be explained by differences in muscle mass.

METHODS

Thirty young (age = 20.7 ± 2.8 y) and 30 postmenopausal (age = 56.3 ± 4.7 y) females completed maximal isometric strength testing (MVIT) and isometric ramp contractions at 40% and 70% MVIT, during which electromyographic signals were collected to quantify the slopes (Slope₄₀; Slope₇₀) and intercepts (Intercept₄₀; Intercept₇₀) of the EMG-T relationships. Muscle mass and quality measurements were also completed.

RESULTS

Postmenopausal females exhibited lower skeletal muscle mass (- 2.3 ± 1.5 kg), fat-free mass index (- 1.1 ± 0.7 kg·m^-2), MVIT (- 17.1 ± 16.3 Nm), phase angle (- 0.5 ± 0.0°), muscle cross-sectional area (- 5.5 ± 1.1 cm²), muscle quality (- 0.1 ± 0.0 a.u), Slope₄₀ (- 0.0003 ± 0.0002 mV·%MVIT^-1), Slope₇₀ (- 0.0003 ± 0.0002 mV·%MVIT^-1), and had a higher echo intensity (+ 9.8 ± 2.8 a.u), Intercept₄₀ (+ 0.001 ± 0.001 mV), and Intercept₇₀ (+ 0.004 ± 0.003 mV) (p ≤ 0.001-0.04) than the young females. The EMG-T relationship variables were correlated with both muscle mass and quality after controlling for bodyweight. When controlling for muscle mass and bodyweight, group differences in the slopes of the EMG-T relationship and muscle strength were eliminated.

CONCLUSION

Muscle mass and quality are primary contributors to the decrements in neuromuscular function observed in postmenopausal versus young females, and the preservation of muscle mass should be prioritized in the years leading up to, during, and immediately after menopause.

Intramuscular EMG For Abstract Myoelectric Control: A Proof Of Concept Study

Myoelectric prostheses are commonly controlled by surface EMG. Many control algorithms, including the user learning-based control paradigm abstract control, benefit from independent control signals. Measuring at the surface of the skin reduces the signal independence through cross talk. To increase the number of independent signals, intramuscular EMG recordings might be a viable alternative for myoelectric control. This proof of concept study investigated if real time abstract myoelectric control is possible with intramuscular measurements. Six participants performed a 4-target and 12-target abstract control task with both surface and intramuscular EMG recordings. The results suggest that intramuscular EMG is suitable for abstract control, and that performance could be increased in the future by stabilizing the amplitude of the processed intramuscular EMG signal.

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.

Towards a Simplified Estimation of Muscle Activation Pattern from MRI and EMG Using Electrical Network and Graph Theory

Muscle functional MRI (mfMRI) is an imaging technique that assess muscles’ activity, exploiting a shift in the T2-relaxation time between resting and active state on muscles. It is accompanied by the use of electromyography (EMG) to have a better understanding of the muscle electrophysiology; however, a technique merging MRI and EMG information has not been defined yet. In this paper, we present an anatomical and quantitative evaluation of a method our group recently introduced to quantify its validity in terms of muscle pattern estimation for four subjects during four isometric tasks. Muscle activation pattern are estimated using a resistive network to model the morphology in the MRI. An inverse problem is solved from sEMG data to assess muscle activation. The results have been validated with a comparison with physiological information and with the fitting on the electrodes space. On average, over 90% of the input sEMG information was able to be explained with the estimated muscle patterns. There is a match with anatomical information, even if a strong subjectivity is observed among subjects. With this paper we want to proof the method’s validity showing its potential in diagnostic and rehabilitation fields.

A Simple Method to Estimate Muscle Currents from HD-sEMG and MRI using Electrical Network and Graph Theory

In the last years the spread of hand prosthetics has fueled the research on the field of signal processing applied on physiologic data. At the state of the art there are different algorithms that allow a precise estimation of hand movements, the majority of whom work just on the electrode space. Even though there are signal processing methods that access single muscle information, they are still premature for a real application on prosthetics. We present a novel method that exploit the information extracted from a magnetic resonance image (MRI) and a single row of high-density surface electromyography (HD-sEMG) electrodes to estimate the muscles currents in the forearm, providing a first experimental application on two simple wrist movements to assess its performance. The results show that the proposed method is able to identify the correct muscle with a single muscle-contraction task, whereas for a 2 muscle task it shows a high variance in the results. The method models the signal propagation from muscles to electrodes using a simple resistive electrical network and uses the graph theory to calculate the muscle currents. It brings a considerably simpler muscle's current estimation method, significantly decreasing the problem complexity, and therefore becoming a potential effective approach for future prosthetics' control.

Estimating Deep Muscles Activation from High Density Surface EMG Using Graph Theory

In the recent years important steps forward have been made in the field of signal processing on muscle signals for hand prosthetics control. At the state of the art different algorithms and techniques allow a precise estimation of hand movements. However, they mostly work exclusively on the electrode space, not seeking for any information about the currents on the contracted muscles.In this study we propose a novel simplified method to estimate the muscles currents in the forearm, along with a first experimental application on two simple movements to assess its performance. We modeled the signal propagation from muscles to electrodes using a purely resistive electrical networks and afterwards apply the graph theory to assess the muscle currents. The proposed method considerably simplify the estimation of muscle's current, decreasing the problem complexity, and therefore potentially it can be a suitable approach for future prosthetics' control.

Modelling the electrical activity of skeletal muscle tissue using a multi-domain approach

Electromyography (EMG) can be used to study the behaviour of the motor neurons and thus provides insights into the physiology of the central nervous system. However, due to the high complexity of neuromuscular control, EMG signals are challenging to interpret. While the exact knowledge of the excitation patterns of a specific muscle within an in vivo experimental setting remains elusive, simulations allow to systematically investigate EMG signals in a controlled environment. Within this context, simulations can provide virtual EMG data, which, for example, can be used to validate and optimise signal analysis methods that aim to estimate the relationship between EMG signals and the output of motor neuron pools. However, since existing methods, which are employed to compute EMG signals, exhibit deficiencies with respect to the physical model itself as well as with respect to numerical aspects, we propose a novel homogenised continuum model that closely resolves the electro-physiological behaviour of skeletal muscle tissue. The proposed model is based on an extension of the well-established bidomain model and includes a biophysically detailed description of the electrical activity within the tissue, which is due to the depolarisation of the muscle fibre membranes. In contrast to all other published EMG models, which assume that the electrical potential field for each muscle fibre can be calculated independently, the proposed model assumes that the electrical potential in the muscle fibres is coupled to the electrical potential in the extracellular space. We show that the newly proposed model is able to simulate realistic EMG signals and demonstrate the potential to employ the predicted virtual EMG signal in order to evaluate the goodness of automated decomposition algorithms.

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.

Model-Based Estimation of Individual Muscle Force Based on Measurements of Muscle Activity in Forearm Muscles During Isometric Tasks

OBJECTIVE

Several forward dynamics estimation approaches have been proposed to estimate individual muscle force. However, characterization of the estimation error that arises when measurements are available only from a subset of the muscles involved in the movement under analysis, as is the case of the forearm muscles, has been limited. Our objectives were: first, to quantify the accuracy of forward-dynamics muscle force estimators for forearm muscles; and second, to develop a muscle force estimator that is accurate even when measurements are available only from a subset of muscles acting on a given joint or segment.

METHODS

We developed a neuromusculoskeletal (NMSK) estimator that integrates forward dynamics estimation with a neural model of muscle cocontraction to estimate individual muscle force during isometric contractions, suitable to operate when measurements are not available for all muscles. We developed a computational framework to assess the effect of physiological variability in muscle cocontraction, cross-talk, and measurement error on the estimator accuracy using a sensitivity analysis. We thus compared the performance of our estimator with that of a standard estimator that neglects the contribution of unmeasured muscles.

RESULTS

The NMSK estimator reduces the estimation error by 25% in average noise conditions. Moreover, the NMSK estimator is robust against physiological variability in muscle cocontraction and outperforms the standard estimator even when the validity of the neural model is compromised.

CONCLUSION AND SIGNIFICANCE

In isometric tasks, the NMSK estimator reduces muscle force estimation error compared to a standard estimator, and may enable future applications involving estimation of forearm muscle force during coordinated movements.

A Comprehensive Mathematical Model of Motor Unit Pool Organization, Surface Electromyography, and Force Generation

Neuromuscular physiology is a vibrant research field that has recently seen exciting advances. Previous publications have focused on thorough analyses of particular aspects of neuromuscular physiology, yet an integration of the various novel findings into a single, comprehensive model is missing. In this article, we provide a unified description of a comprehensive mathematical model of surface electromyographic (EMG) measurements and the corresponding force signal in skeletal muscles, both consolidating and extending the results of previous studies regarding various components of the neuromuscular system. The model comprises motor unit (MU) pool organization, recruitment and rate coding, intracellular action potential generation and the resulting EMG measurements, as well as the generated muscular force during voluntary isometric contractions. Mathematically, it consists of a large number of linear PDEs, ODEs, and various stochastic nonlinear relationships, some of which are solved analytically, others numerically. A parameterization of the electrical and mechanical components of the model is proposed that ensures a physiologically meaningful EMG-force relation in the simulated signals, in particular taking the continuous, size-dependent distribution of MU parameters into account. Moreover, a novel nonlinear transformation of the common drive model input is proposed, which ensures that the model force output equals the desired target force. On a physiological level, this corresponds to adjusting the rate coding model to the force generating capabilities of the simulated muscle, while from a control theoretic point of view, this step is equivalent to an exact linearizing transformation of the controlled neuromuscular system. Finally, an alternative analytical formulation of the EMG model is proposed, which renders the physiological meaning of the model more clear and facilitates a mathematical proof that muscle fibers in this model at no point in time represent a net current source or sink. A consistent description of a complete physiological model as presented here, including thorough justification of model component choices, will facilitate the use of these advanced models in future research. Results of a numerical simulation highlight the model's capability to reproduce many physiological effects observed in experimental measurements, and to produce realistic synthetic data that are useful for the validation of signal processing algorithms.

Single finger movements in the aging hand: changes in finger independence, muscle activation patterns and tendon displacement in older adults

With aging, hand mobility and manual dexterity decline, even under healthy circumstances. To assess how aging affects finger movement control, we compared elderly and young subjects with respect to (1) finger movement independence, (2) neural control of extrinsic finger muscles and (3) finger tendon displacements during single finger flexion. In twelve healthy older (age 68-84) and nine young (age 22-29) subjects, finger kinematics were measured to assess finger movement enslaving and the range of independent finger movement. Muscle activation was assessed using a multi-channel electrode grid placed over the flexor digitorum superficialis (FDS) and the extensor digitorum (ED). FDS tendon displacements of the index, middle and ring fingers were measured using ultrasound. In older subjects compared to the younger subjects, we found: (1) increased enslaving of the middle finger during index finger flexion (young: 25.6 ± 12.4%, elderly: 47.0 ± 25.1%; p = 0.018), (2) a lower range of independent movement of the index finger (young_middle = 74.0%, elderly_middle: 45.9%; p < 0.001), (3) a more evenly distributed muscle activation pattern over the finger-specific FDS and ED muscle regions and (4) a lower slope at the beginning of the finger movement to tendon displacement relationship, presenting a distinct period with little to no tendon displacement. Our study indicates that primarily the movement independence of the index finger is affected by aging. This can partly be attributed to a muscle activation pattern that is more evenly distributed over the finger-specific FDS and ED muscle regions in the elderly.

Simplified parametric models of the dielectric properties of brain and muscle tissue during electrical stimulation

Parametric models are commonly used to describe the dispersive dielectric properties of biological tissues. While distinct regions of dispersion have been identified, the relative contribution of each during electrical stimulation is unknown. This study quantified the contribution of individual poles in parametric models of brain and muscle dielectric properties during electrical stimulation. The effect on the extracellular voltage waveform and threshold current for nerve stimulation of selectively removing subsets of poles from Cole-Cole and Debye models was examined. Errors were introduced when dispersions below 100 kHz were removed in both brain and muscle tissue. Poles below 1 kHz influenced the amplitude of the extracellular voltage waveform and the predicted minimum stimulation current. Poles between 1 kHz and 100 kHz influenced the waveform shape, with a minor effect on stimulus amplitude. The results confirm that low frequency dispersion in conductivity and permittivity can fundamentally influence the electric field and neural response during stimulation and provide insight into the relative contribution of the different dispersive regimes. Furthermore, they provide justification for for simplifying parametric models of dielectric properties through the removal of high frequency poles above 100 kHz which could improve the efficiency of time-domain solvers for simulations involving time-varying or aperiodic stimuli as may be required for certain closed-loop stimulation paradigms.

Quantum theory of mass potentials

Probabilistic formalism of quantum mechanics is used to quantitatively link the global scale mass potential with the underlying electrical activity of excitable cells. Previous approaches implemented methods of classical physics to reconstruct the mass potential in terms of explicit physical models of participating cells and the volume conductor. However, the multiplicity of cellular processes with extremely intricate mixtures of deterministic and random factors prevents the creation of consistent biophysical parameter sets. To avoid the uncertainty inherent in physical attributes of cell ensembles, we undertake here a radical departure from deterministic equations of classical physics, instead applying the probabilistic reasoning of quantum mechanics. Crucial steps include: (1) the relocation of the elementary bioelectric sources from a cellular to a molecular level; (2) the creation of microscale particle models in terms of a non-homogenous birth-and-death process. To link the microscale processes with macroscale potentials, time-frequency analysis was applied for estimation of the empirical characteristic functions for component waveforms of electroencephalogram (EEG), eye-blink electromyogram (EMG), and electrocardiogram (ECG). We describe universal models for the amplitude spectra and phase functions of functional components of mass potentials. The corresponding time domain relationships disclose the dynamics of mass potential components as limit distribution functions produced by specific microscale transients. The probabilistic laws governing the microscale machinery, founded on an empirical basis, are presented. Computer simulations of particle populations with time dependent transition probabilities reveal that hidden deterministic chaos underlies development of the components of mass potentials. We label this kind of behaviour “transient deterministic chaos”.

Optimal spatio-temporal filter for the reduction of crosstalk in surface electromyogram

OBJECTIVE

Crosstalk can pose limitations to the applications of surface electromyogram (EMG). Its reduction can help in the identification of the activity of specific muscles. The selectivity of different spatial filters was tested in the literature both in simulations and experiments: their performances are affected by many factors (e.g. anatomy, conduction properties of the tissues and dimension/location of the electrodes); moreover, they reduce crosstalk by decreasing the detection volume, recording data that represent only the activity of a small portion of the muscle of interest. In this study, an alternative idea is proposed, based on a spatio-temporal filter.

APPROACH

An adaptive method is applied, which filters both in time and among different channels, providing a signal that maximally preserves the energy of the EMG of interest and discards that of nearby muscles (increasing the signal to crosstalk ratio, SCR).

MAIN RESULTS

Tests with simulations and experimental data show an average increase of the SCR of about 2 dB with respect to the single or double differential data processed by the filter. This allows to reduce the bias induced by crosstalk in conduction velocity and force estimation.

SIGNIFICANCE

The method can be applied to few channels, so that it is useful in applicative studies (e.g. clinics, gate analysis, rehabilitation protocols with EMG biofeedback and prosthesis control) where limited and not selective information is usually available.

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).

Predicting electromyographic signals under realistic conditions using a multiscale chemo-electro-mechanical finite element model

This paper presents a novel multiscale finite element-based framework for modelling electromyographic (EMG) signals. The framework combines (i) a biophysical description of the excitation-contraction coupling at the half-sarcomere level, (ii) a model of the action potential (AP) propagation along muscle fibres, (iii) a continuum-mechanical formulation of force generation and deformation of the muscle, and (iv) a model for predicting the intramuscular and surface EMG. Owing to the biophysical description of the half-sarcomere, the model inherently accounts for physiological properties of skeletal muscle. To demonstrate this, the influence of membrane fatigue on the EMG signal during sustained contractions is investigated. During a stimulation period of 500 ms at 100 Hz, the predicted EMG amplitude decreases by 40% and the AP propagation velocity decreases by 15%. Further, the model can take into account contraction-induced deformations of the muscle. This is demonstrated by simulating fixed-length contractions of an idealized geometry and a model of the human tibialis anterior muscle (TA). The model of the TA furthermore demonstrates that the proposed finite element model is capable of simulating realistic geometries, complex fibre architectures, and can include different types of heterogeneities. In addition, the TA model accounts for a distributed innervation zone, different fibre types and appeals to motor unit discharge times that are based on a biophysical description of the α motor neurons.

A practice of caution: spontaneous action potentials or artifactual spikes?

Background

High density surface electromyogram (EMG) techniques with electrode arrays have been used to record spontaneous muscle activity, which is important, both for supporting the diagnosis of neuromuscular diseases and for laboratory based neurophysiological investigations. This short report addresses a practical issue we have experienced during recording of spontaneous muscle activity using electrode arrays from subjects with major neuromuscular disorders.

Findings

We show that recording artifacts can appear similar to spontaneous action potential spikes. Moreover, a causal filter may induce asymmetric distortions of an artifact and thus confuse it with a real action potential spike. As a consequence, for a single channel surface EMG recording, it might be difficult to judge whether a voltage transient is a real action potential or an artifact. Further investigation of the signal distributions among other channels of the array can be used to reach a more confident judgment.

Conclusions

During examination of spontaneous muscle activity using electrode arrays, caution is required for differentiation of physiological signals from artifactual spikes, which is important for accurate extraction of diagnostic or investigatory information.

Influence of different geometric representations of the volume conductor on nerve activation during electrical stimulation

Volume conductor models with different geometric representations, such as the parallel layer model (PM), the cylindrical layer model (CM), or the anatomically based model (AM), have been employed during the implementation of bioelectrical models for electrical stimulation (FES). Evaluating their strengths and limitations to predict nerve activation is fundamental to achieve a good trade-off between accuracy and computation time. However, there are no studies aimed at clarifying the following questions. (1) Does the nerve activation differ between CM and PM? (2) How well do CM and PM approximate an AM? (3) What is the effect of the presence of blood vessels and nerve trunk on nerve activation prediction? Therefore, in this study, we addressed these questions by comparing nerve activation between CM, PM, and AM models by FES. The activation threshold was used to evaluate the models under different configurations of superficial electrodes (size and distance), nerve depths, and stimulation sites. Additionally, the influences of the sciatic nerve, femoral artery, and femoral vein were inspected for a human thigh. The results showed that the CM and PM had a high error rate, but the variation of the activation threshold followed the same tendency for electrode size and interelectrode distance variation as AM.

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.

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 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.

Distribution of electrical stimulation current in a planar multilayer anisotropic tissue

This study analytically addresses the problem of neuromuscular electrical stimulation for a planar, multilayer, anisotropic model of a physiological tissue (referred to as volume conductor). Both conductivity and permittivity of the volume conductor are considered, including dispersive properties. The analytical solution is obtained in the 2-D Fourier transform domain, transforming in the planes parallel to the volume conductor surface. The model is efficient in terms of computational cost, as the solution is analytical (only numerical Fourier inversion is needed). It provides the current distribution in a physiological tissue induced by an electrical current delivered at the skin surface. Three representative examples of application of the model are considered. 1) The simulation of stimulation artefact during transcutaneous electrical stimulation and EMG detection. Only the effect of the volume conductor is considered, neglecting the other sources of artefact (such as the capacitive coupling between the stimulating and recording electrodes). 2) The simulation of the electrical current distribution within the muscle and the low-pass filter effect of the volume conductor on sinusoidal stimulation currents with different stimulation frequencies. 3) The estimation of the amplitude modulated current distribution within the muscle for interferential stimulation. The model is devoted to the simulation of neuromuscular stimulation, but the same method could be applied in other fields in which the estimation of the electrical current distribution in a medium induced by the injection of a current from the boundary of the medium is of interest.

Targeted reinnervation for improved prosthetic function

Target motor reinnervation can produce additional myoelectric control signals for improved powered prosthesis control. This reinnervation allows simultaneous operation of multiple functions in an externally powered prosthesis with physiologically appropriate pathways, and it provides more intuitive control than is possible with conventional myoelectric prostheses. Target sensory reinnervation has the potential to provide the sensory feed-back to the amputee that feels like it is in the missing limb. This concept has great potential for improving the function of people with upper limb amputations, especially for high-level amputations, in which the disability is greatest. It is hoped that future research will develop the technique further and build synergistically with other exciting research areas.