The Position of Innervation Zones in the Biceps Brachii Investigated by Surface Electromyography

A linear surface electrode array placed along the muscle fibers detects motor unit action potentials propagating bilaterally to the tendons. The location of the propagation source is presumed to mark an innervation zone. We developed a computer program, which automatically determined the potential source by applying a correlation calculation and a linear regression to the recorded signals. The spacing between the contacts in the electrode array was 5.0 mm, whereas for some recordings the histogram indicating the position of estimated source had a sharp peak concentrated in a 1.0 mm area. In the biceps brachii some subjects were found to have two innervation zones separated by 10-20 mm. The peaks corresponding to the innervation zones differentially changed their histogram scores according to the contraction force. We also constructed an electrode assembly, which had four columns of the linear electrode arrays, and clarified the distribution of the innervation zones in the biceps brachii.     

Modeling of surface myoelectric signals. II. Model-based signalinterpretation

Experimental electromyogram (EMG) data from the human biceps brachii were simulated using the model described in [10] of this work. A multichannel linear electrode array, spanning the length of the biceps, was used to detect monopolar and bipolar signals, from which double differential signals were computed, during either voluntary or electrically elicited isometric contractions. For relatively low-level voluntary contractions (10%-30% of maximum force) individual firings of three to four-different motor units were identified and their waveforms were closely approximated by the model. Motor unit parameters such as depth, size, fiber orientation and length, location of innervation and tendonous zones, propagation velocity, and source width were estimated using the model. Two applications of the model are described. The first analyzes the effects of electrode rotation with respect to the muscle fiber direction and shows the possibility of conduction velocity (CV) over- and under-estimation. The second focuses on the myoelectric manifestations of fatigue during a sustained electrically elicited contraction and the interrelationship between muscle fiber CV, spectral and amplitude variables, and the length of the depolarization zone. It is concluded that a) surface EMG detection using an electrode array, when combined with a model of signal propagation, provides a useful method for understanding the physiological and anatomical determinants of EMG waveform characteristics and b) the model provides a way for the interpretation of fatigue plots.     

Topographical map of innervation zones within single motor unitsmeasured with a grid surface electrode

A grid electrode with closely spaced contacts was developed to measure the spatial distribution of motor unit action potentials on a skin surface. This electrode was used to estimate the configuration of innervation zones. The derived action potentials showed waveforms originating from the middle length of the muscle belly and propagating bidirectionally along the muscle fibers. The position of the innervation zones was estimated from the source of the propagation. By isolating action potentials from single motor units, the innervation zones for individual motor units were defined. Studies were performed in the biceps brachii of three normal subjects. The innervation zones comprised one to four separate regions and spread over up to 20 mm along the length of muscle fibers. The number and the spreading area of innervation zones varied with the subject and with the motor unit     

A novel approach for estimating muscle fiber conduction velocity by spatial and temporal filtering of surface EMG signals

We describe a new method for the estimation of muscle fiber conduction velocity (CV) from surface electromyography (EMG) signals. The method is based on the detection of two surface EMG signals with different spatial filters and on the compensation of the spatial filtering operations by two temporal filters (with CV as unknown parameter) applied to the signals. The transfer functions of the two spatial filters may have different magnitudes and phases, thus the detected signals have not necessarily the same shape. The two signals are first spatially and then temporally filtered and are ideally equal when the CV value selected as a parameter in the temporal filters corresponds to the velocity of propagation of the detected action potentials. This approach is the generalization of the classic spectral matching technique. A theoretical derivation of the method is provided together with its fast implementation by an iterative method based on the Newton's method. Moreover, the lowest CV estimate among those obtained by a number of filter pairs is selected to reduce the CV bias due to nonpropagating signal components. Simulation results indicate that the method described is less sensitive than the classic spectral matching approach to the presence of nonpropagating signals and that the two methods have similar standard deviation of estimation in the presence of additive, white, Gaussian noise. Finally, experimental signals have been collected from the biceps brachii muscle of ten healthy male subjects with an adhesive linear array of eight electrodes. The CV estimates depended on the electrode location with positive bias for the estimates from electrodes close to the innervation or tendon regions, as expected. The proposed method led to significantly lower bias than the spectral matching method in the experimental conditions, confirming the simulation results.     

Continuous monitoring of sonomyography, electromyography and torque generated by normal upper arm muscles during isometric contraction: sonomyography assessment for arm muscles

The aim of this study is to demonstrate the feasibility of using the continuous signals about the thickness and pennation angle changes of muscles detected in real-time from ultrasound images, named as sonomyography (SMG), to characterize muscles under isometric contraction, along with synchronized surface electromyography (EMG) and generated torque signals. The right biceps brachii muscles of seven normal young adult subjects were tested.We observed that exponential functions could well represent the relationships between the normalized EMG root-mean-square (RMS) and the torque, the RMS and the muscle deformation SMG, and the RMS and the pennation angle SMG for the data of the contraction phase, with exponent coefficients of 0.0341 +/- 0.0148 (Mean SD), 0.0619 +/- 0.0273, and 0.0266 +/- 0.0076, respectively. In addition, the preliminary results also demonstrated linear relationships between the normalized torque and the muscle deformation as well as the pennation angle with the ratios of 9 .79 +/- 3.01 and 2.02 +/- 0.53, respectively. The overall mean R2 for the regressions was approximately 0.9 and the overall mean relative root mean square error (RRMSE) smaller than 15%. The potential values of SMG together with EMG to provide a more comprehensive assessment for the muscle functions should be further investigated with more subjects and more muscle groups.     

Spatial Filtering of Noninvasive Multielectrode EMG: Part I?Introduction to Measuring Technique and Applications

Complementary to its conventional applications, surface EMG is also suited to gain more detailed information on the functional state of a muscle, when measurement configurations with smaller pickup areas are used. A new category of suitable measurement configurations is obtained by application of the spatial filtering principle to electromyography. In a spatial filter unit, the signals of several recording electrodes are combined to form one output signal channel. The filter characteristic is determined by the weighting factors used and by the geometrical arrangement of the electrodes. Extended multielectrode arrays and multichannel recording make possible the detection of correlated excitations at different sites of the muscle. Even in high levels of muscle contraction, single motor unit impulses that are suitably shaped by filtering can be repeatedly recognized in the surface EMG signal. In clinical studies, pathologically shaped impulses have been identified indicating multiple innervation zones. The initiation and the propagation of excitation within single motor units can be detected with improved accuracy even from very small muscles.     

Assessment of average muscle fiber conduction velocity from surface EMG signals during fatiguing dynamic contractions

In this paper, we propose techniques of surface electromyographic (EMG) signal detection and processing for the assessment of muscle fiber conduction velocity (CV) during dynamic contractions involving fast movements. The main objectives of the study are: 1) to present multielectrode EMG detection systems specifically designed for dynamic conditions (in particular, for CV estimation); 2) to propose a novel multichannel CV estimation method for application to short EMG signal bursts; and 3) to validate on experimental signals different choices of the processing parameters. Linear adhesive arrays of electrodes are presented for multichannel surface EMG detection during movement. A new multichannel CV estimation algorithm is proposed. The algorithm provides maximum likelihood estimation of CV from a set of surface EMG signals with a window limiting the time interval in which the mean square error (mse) between aligned signals is minimized. The minimization of the windowed mse function is performed in the frequency domain, without limitation in time resolution and with an iterative computationally efficient procedure. The method proposed is applied to signals detected from the vastus laterialis and vastus medialis muscles during cycling at 60 cycles/min. Ten subjects were investigated during a 4-min cycling task. The method provided reliable assessment of muscle fatigue for these subjects during dynamic contractions.     

Motor unit conduction velocity distribution estimation from evoked motor responses

Action potentials travel along the muscle fibers with a specific conduction velocity that depends on their structural and functional properties. Only the estimation of muscle conduction velocity distribution (MCVD) may be able to depict this propagation heterogeneity. Based on the method proposed by Cummins et al. (Electroenceph Clin Neurophysiol, 46:647-658, 1979) to estimate nerve conduction velocity distribution (NCVD), the present paper proposes a method that modifies the Cummins' approach to make it suitable for MCVD estimation from electrically evoked motor responses. The MCVD estimation algorithm was first assessed by means of simulated signals in order to control all signal features during the optimization process. Simulations showed that estimated distributions were very close to the true ones when taking into account the specificities of the muscle action potential, due to its generation and extinction (MSE divided by 5 on distribution standard deviation). This method was then applied to real signals. Elicited motor responses were recorded on the biceps brachii of healthy subjects either during repeated maximal stimulations at 20 Hz or during increasing intensity stimulations at 1 Hz. MCVD estimates were used to analyze fatigue and motor unit recruitment processes, respectively.     

Simulation of surface EMG signals generated by muscle tissues with inhomogeneity due to fiber pinnation

Surface electromyographic (EMG) signal modeling has important applications in the interpretation of experimental EMG data. Most models of surface EMG generation considered volume conductors homogeneous in the direction of propagation of the action potentials. However, this may not be the case in practice due to local tissue inhomogeneities or to the fact that there may be groups of muscle fibers with different orientations. This study addresses the issue of analytically describing surface EMG signals generated by bi-pinnate muscles, i.e., muscles which have two groups of fibers with two orientations. The approach will also be adapted to the case of a muscle with fibers inclined in the depth direction. Such muscle anatomies are inhomogeneous in the direction of propagation of the action potentials with the consequence that the system can not be described as space invariant in the direction of source propagation. In these conditions, the potentials detected at the skin surface do not travel without shape changes. This determines numerical issues in the implementation of the model which are addressed in this work. The study provides the solution of the nonhomogenous, anisotropic problem, proposes an implementation of the results in complete surface EMG generation models (including finite-length fibers), and shows representative results of the application of the models proposed.     

Discriminant analysis of changes in human muscle function wheninteracting with an assistive aid

The authors examines the changes of human muscle function when the arm's simple antagonist tricep-bicep muscle pair is aided by a mechanical assistive device. The assistive aid changes how the muscles are used, and this shift in muscle activity is determined objectively by using discriminant analysis. Surface EMG signals from the biceps and triceps provide the electric signals. Four types of primitive arm movements are used in the detection of the muscle changes     

Autoregressive Modeling of Surface EMG and Its Spectrum with Application to Fatigue

The following is an investigation of the ability of the autoregressive (AR) model to describe the spectrum of the processes underlying the recorded surface EMG. Surface EMG (SEMG) spectrum is influenced by two major factors; one attributed to the motor units (MU) firing rate and the second, the higher frequency one, to the morphology of the action potentials (AP) traveling along the muscle fiber. In the present paper, SEMG measurements were carried out on the biceps brachii muscle with fixed surface electrodes arrangement and isotonic conditions. Sufficient averaging of 0.5 s segments enabled the identification of the low-frequency peak related to the firing rates of the MU's.     

Selectivity of spatial filters for surface EMG detection from the tibialis anterior muscle

Many spatial filters have been proposed for surface electromyographic (EMG) signal detection. Although theoretical and modeling predictions on spatial selectivity are available, there are no extensive experimental validations of these techniques based on single motor unit (MU) activity detection. The aim of this study was to compare spatial selectivity of one- and two-dimensional (1-D and 2-D) spatial filters for EMG signal detection. Intramuscular and surface EMG signals were recorded from the tibialis anterior muscle of ten subjects. The simultaneous use of intramuscular wire and surface recordings (with the spike triggered averaging technique) allowed investigation of the activity of single MUs at the skin surface. The surface EMG signals were recorded with a grid of point electrodes (3 x 3 electrodes) and a ring electrode system at 15 locations over the muscle, with the wires detecting signals from the same intramuscular location. For most subjects, it was possible to classify, from the intramuscular recordings, the activity of the same MUs for all the contractions. The surface EMG signals were averaged with the intramuscularly detected MU action potentials as triggers. In this way, eight spatial filters--longitudinal and transversal, single and double differential (LSD, TSD, LDD, TDD), Laplacian (NDD), inverse binomial filter of the second order (IB2), inverse rectangle filter (IR), and differential ring system (C1)--could be compared on the basis of their spatial selectivity. The distance from the source (transversal with respect to the muscle fiber orientation) after which the surface detected potential did not exceed +/- 5% of the maximal peak-to-peak amplitude (detection distance) was statistically smaller for the 2-D systems and TDD than for the other filters. The MU action potential duration was significantly shorter with LDD and with the 2-D systems than with the other filters. The 2-D filters investigated (including C1) showed very similar performance and were, thus, considered equivalent from the point of view of spatial selectivity.     

An Application of Signal Processing Techniques to the Study of Myoelectric Signals

This paper describes the use of power spectral density and cumulative power functions in the examination of the electromyogram (EMG). The EMG signals were obtained with surface electrodes from two muscles, the flexor pollicis brevis and the extensor digitorum, in four subjects. Each muscle was studied at two levels of contraction, both before and during fatigue. The power spectral density functions are compared, using a cumulative power difference function and the mean frequency of the spectrum, to determine differences between loading conditions in an individual muscle, before and during fatigue, between different muscles, between individuals (same muscle), and combinations of these conditions.     

Independent component analysis of high-density electromyography in muscle force estimation

Accurate force prediction from surface electromyography (EMG) forms an important methodological challenge in biomechanics and kinesiology. In a previous study (Staudenmann et al., 2006), we illustrated force estimates based on analyses lent from multivariate statistics. In particular, we showed the advantages of principal component analysis (PCA) on monopolar high-density EMG (HD-EMG) over conventional electrode configurations. In the present study, we further improve force estimates by exploiting the correlation structure of the HD-EMG via independent component analysis (ICA). HD-EMG from the triceps brachii muscle and the extension force of the elbow were measured in 11 subjects. The root mean square difference (RMSD) and correlation coefficients between predicted and measured force were determined. Relative to using the monopolar EMG data, PCA yielded a 40% reduction in RMSD. ICA yielded a significant further reduction of up to 13% RMSD. Since ICA improved the PCA-based estimates, the independent structure of EMG signals appears to contain relevant additional information for the prediction of muscle force from surface HD-EMG.     

On Modeling the Single Motor Unit Action Potential

A model has been proposed for the generation of single motor unit potentials routinely observed in the clinical EMG examination of the normal biceps brachii muscle. A dipole representation was chosen for the single fiber activity. The motor unit was constructed from a uniform random array of single fibers. Motor unit potentials generated by this array have been observed at various distances both inside and outside the array. The effects of single fiber dipole axial dispersions on the potentials observed at increasing distances from the array have also been investigated. Motor unit potentials generated by the model have been compared with existing data from multielectrode studies in the biceps brachii.     

Modeling of surface myoelectric signals. I. Model implementation

The relationships between the parameters of active motor units (MU's) and the features of surface electromyography (EMG) signals have been investigated using a mathematical model that represents the surface EMG as a summation of contributions from the single muscle fibers. Each MU has parallel fibers uniformly scattered within a cylindrical volume of specified radius embedded in an anisotropic medium. Two action potentials, each modeled as a current tripole, are generated at the neuromuscular junction, propagate in opposite directions and extinguish at the fiber-tendon endings. The neuromuscular junctions and fiber-tendon endings are uniformly scattered within regions of specified width. Muscle fiber conduction velocity and average fiber length to the right and left of the center of the innervation zone are also specified. The signal produced by MU's with different geometries and conduction velocities are superimposed. Monopolar, single differential and double differential signals are computed from electrodes placed in equally spaced locations on the surface of the muscle and are displayed as functions of any of the model's parameters. Spectral and amplitude variables and conduction velocity are estimated from the surface signals and displayed as functions of any of the model's parameters. The influence of fiber-end effects, electrode misalignment, tissue anisotropy, MU's location and geometry are discussed. Part II of this paper will focus on the simulation and interpretation of experimental signals.     

A comparison of the effects of electrode implantation and targeting on pattern classification accuracy for prosthesis control

The use of surface versus intramuscular electrodes as well as the effect of electrode targeting on pattern-recognition-based multifunctional prosthesis control was explored. Surface electrodes are touted for their ability to record activity from relatively large portions of muscle tissue. Intramuscular electromyograms (EMGs) can provide focal recordings from deep muscles of the forearm and independent signals relatively free of crosstalk. However, little work has been done to compare the two. Additionally, while previous investigations have either targeted electrodes to specific muscles or used untargeted (symmetric) electrode arrays, no work has compared these approaches to determine if one is superior. The classification accuracies of pattern-recognition-based classifiers utilizing surface and intramuscular as well as targeted and untargeted electrodes were compared across 11 subjects. A repeated-measures analysis of variance revealed that when only EMG amplitude information was used from all available EMG channels, the targeted surface, targeted intramuscular, and untargeted surface electrodes produced similar classification accuracies while the untargeted intramuscular electrodes produced significantly lower accuracies. However, no statistical differences were observed between any of the electrode conditions when additional features were extracted from the EMG signal. It was concluded that the choice of electrode should be driven by clinical factors, such as signal robustness/stability, cost, etc., instead of by classification accuracy.     

Application of higher order statistics techniques to EMG signals to characterize the motor unit action potential

The electromyographic (EMG) signal provides information about the performance of muscles and nerves. At any instant, the shape of the muscle signal, motor unit action potential (MUAP), is constant unless there is movement of the position of the electrode or biochemical changes in the muscle due to changes in contraction level. The rate of neuron pulses, whose exact times of occurrence are random in nature, is related to the time duration and force of a muscle contraction. The EMG signal can be modeled as the output signal of a filtered impulse process where the neuron firing pulses are assumed to be the input of a system whose transfer function is the motor unit action potential. Representing the neuron pulses as a point process with random times of occurrence, the higher order statistics based system reconstruction algorithm can be applied to the EMG signal to characterize the motor unit action potential. In this paper, we report results from applying a cepstrum of bispectrum based system reconstruction algorithm to real wired-EMG (wEMG) and surface-EMG (sEMG) signals to estimate the appearance of MUAPs in the Rectus Femoris and Vastus Lateralis muscles while the muscles are at rest and in six other contraction positions. It is observed that the appearance of MUAPs estimated from any EMG (wEMG or sEMG) signal clearly shows evidence of motor unit recruitment and crosstalk, if any, due to activity in neighboring muscles. It is also found that the shape of MUAPs remains the same on loading.     

On surface EMG spectral characterization and its application to diagnostic classification

Surface EMG was recorded from the biceps with fixed muscle length at S0 percent maximal voluntary contraction. The signal bandpass was 10-230 Hz where most of the surface EMG energy is located. The signal sampled at 500 Hz was found to have a changing spectrum. Stationary segments of 500 ms were subject to linear prediction mathematics to model the system.     

The relationship between the surface EMG and force transients in muscle: Simulation and experimental studies

The surface electromyogrmn (EMG) is an easily measured signal which when quantified by present techniques is a reliable measure of whether a muscle is active, a fairly reliable measure of steady state force and a rather unreliable measure of force transients in muscle. There is a real need for a reliable indicator of dynamic changes in muscle activity for the control of prosthetics, in diagnosis of neuromuscular diseases, in studies of the motor control system and in fundamental studies of muscle mechanics. This paper outlines the principles underlying the development of force and the EMG in muscle. The EMG is a poor estimate of muscle force since it is the result of the linear superposition of biphasic action potentials which result in an interference pattern. This in turn is dependent on the details of the firing intervals for each motor unit, whereas the force is quite insensitive to these details. Experimental results for the human biceps brachii are described in which it was not possible to obtain a consistent estimate of muscle active state from the EMG. An extensive computer simulation was used to explore the relationship between EMG and force under a variety of assumptions. The conclusion is that it is technically impossible to obtain consistent estimates of muscle force (or active state) unless a filter with a time constant of 300 ms is applied to the rectified EMG. This is inconsistent with the estimation of active state for voluntary contractions with observed rise time constants of 30-70 ms. It is susgested that the only solution is to repeat an experiment many times and average the rectified EMG. Unfortunately, in practice it is difficult to repeat certain aspects of voluntary muscular contractions.