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.     

Simulation Techniques in Electromyography

A motor unit action potential (MUAP) recorded in clinical electromyography (EMG) is the spatial and temporal summation of the action potentials (AP's) from all muscle fibers in a motor unit (MU). An important determinant of MUAP waveform characteristics is the size of the recording electrode. In this paper, we have described the use of a modified line source model of single muscle fiber action potentials to simulate MUAP's as recorded by single fiber (SF) EMG, concentric needle (CN) EMG, and macro-EMG electrodes. Results indicate that SFEMG recordings from a normal MU contain mainly the AP's of the closest one to three muscle fibers of the MU. The amplitude, area, and duration of the simulated CNEMG MUAP's are determined mainly by the number and size of muscle fibers within a semicircular territory of 0.5, 1.5, and 2.5 mm, respectively, around the tip of the electrode. The amplitude and area of simulated macro-EMG MUAP's increase with the number of muscle fibers in the MU.     

Decomposition of multiunit electromyographic signals

We have developed a comprehensive technique to identify single motor unit (SMU) potentials and to decompose overlapped electromyographic (EMG) signals into their constituent SMU potentials. This technique is based on one-channel EMG recordings and is easily implemented for many clinical EMG tests. There are several distinct features of our technique: 1) it measures waveform similarity of SMU potentials in the wavelet domain, which gives this technique significant advantages over other techniques; 2) it classifies spikes based on the nearest neighboring algorithm, which is less sensitive to waveform variation; 3) it can effectively separate compound potentials based on a maximum signal energy deduction algorithm, which is fast and relatively reliable; and 4) it also utilizes the information on discharge regularities of SMU's to help correct possible decomposition errors. The performance of this technique has been evaluated by using simulated EMG signals composed of up to eight different discharging SMU's corrupted with white noise, and also by using real EMG signals recorded at levels up to 50% maximum voluntary contraction. We believe that it is a very useful technique to study SMU discharge patterns and recruitment of motor units in patients with neuromuscular disorders in clinical EMG laboratories.     

Use of Signal Representation to Identify Abnormal Motor Unit Potentials in Macro EMG

Macro electromyography (EMG) is a recently described recording technique allowing a nonselective recording of motor unit activity. The pick-up area of the electrode, the cannula of a modified single-fiber electrode, covers the entire motor unit territory. The motor unit potential (MUP) is obtained by averaging the cannula signals that are time locked to a single-fiber action potential. The MUP waveform is relatively insensitive to the position of the electrode within the motor unit. The amplitude of the MUP has been used as a measure of the size of the motor unit and normal values for the individual and median MUP amplitude have been defined.     

Motor Unit Firing Rate During Static Contraction Indicated by the Surface EMG Power Spectrum

In this study, power spectral density functions (PSDF's) were computed of interference EMG of various facial and jaw-elevator muscles during nonfatiguing submaximal static contractions, recorded with surface electrodes. A distinct peak was found in the PSDF's in the frequency region below 40 Hz. It was shown that the peak was due to genuine EMG activity and that it could not be considered as an artifact, which was caused by electrode displacements during contraction. An increase of contraction strength resulted in a shift of the peak to higher frequencies and a decrease of peak amplitude relative to the power spectral estimates above 40 Hz, which were shown to be determined by the shape of the motor unit (MU) action potentials. In accordance with mathematical models of the EMG PSDF, it was demonstrated that the peak indicates the dominant firing rate of the sampled MU's. Our results suggest that this can be defined as the firing rate of the first recruited low-threshold MU's, which may be expected to dominate the interference EMG signal because of their preponderance in number. The data further suggest that the peak can be more readily observed in PSDF's of facial and jaw-elevator muscles than in PSDF's of limb muscles. This might be related to differences in MU firing statistics.     

Nonlinear Spatial Filtering of Multichannel Surface Electromyogram Signals During Low Force Contractions

This study introduces the application of nonlinear spatial filters to help identify single motor unit discharge from multiple channel surface electromyogram (EMG) signals during low force contractions. The nonlinear spatial filters simultaneously take into account the instantaneous amplitude and frequency information of a signal. This property was used to enhance motor unit action potentials (MUAPs) in the surface EMG record. The advantages of nonlinear spatial filtering for surface MUAP enhancement were investigated using both simulation and experimental approaches. The simulation results indicate that when compared with various linear spatial filters, nonlinear spatial filtering achieved higher SNR and higher kurtosis of the surface EMG distribution. Over a broad range of SNR and kurtosis levels for the input signal, nonlinear spatial filters achieved at least 32 times greater SNR and 11% higher kurtosis for correlated noise, and at least 15 times greater SNR and 1.7 times higher kurtosis for independent noise, across electrode array channels. The improvements offered by nonlinear spatial filters were further documented by applying them to experimental surface EMG array recordings. Compared with linear spatial filters, nonlinear spatial filters achieved at least nine times greater SNR and 25% higher kurtosis. It follows that nonlinear spatial filters represent a potentially useful supplement to linear spatial filters for detection of motor unit activity in surface EMG at low force contractions.     

An Analysis of the Electromyogram by Fourier, Simulation and Experimental Techniques

The electromyogram of a single motor unit is studied by considering it as a time function defined by a convolution integral where a point process input passes through a filter whose impulse response is the shape of a single motor unit action potential. The interspike intervals are assumed to be normally distributed, independent random variables. Simulation is performed on a digital computer. The theoretical analysis shows that the absolute value of the ensemble average of the Fourier transform of the simulated EMG approaches the absolute value of the Fourier transform of the motor unit potential. This has been confirmed by simulation except at the very low end of the spectrum. These results are compared with the Fourier transforms of the recorded surface EMG data from human muscles.     

An algorithm for sequential signal estimation and system identification for EMG signals

This paper presents a new algorithm for optimal adaptation of the signal templates of a matched filter bank used in the detection of the motor unit action potential waveforms (abbreviated as MUAP's) in an electromyogram (EMG). It is of interest, for clinical diagnosis and therapy, to detect as many MUAP's as possible in a single measurement, and to determine for each motor unit the repetition. rate of its respective MUAP. For this purpose, we have developed a computer program which, in addition to other subprograms, contains the adaptive filter bank mentioned above. The templates in this fllter bank have to be adapted to nonpredetermined changes in measurement conditions such as the movement of the needle electrode inserted in the muscle. In the present paper, the above templates are estimated by means of a "tumbling algorithm," so called because the successive MUAP's from a given motor unit are used as noisy data vectors in a time-varying Kalman filter-predictor framework, which alternately estinates their evolving shapes and identifies the time-varying parameters of the model generating them. The algorithm has been applied with success to synthetic and real EMG data.     

A New Framework and Computer Program for Quantitative EMG Signal Analysis

Techniques for analyzing electromyographic signals, which estimate and detect potentials caused by active motor units in human striated muscles, are described. A framework within which these techniques are incorporated into a computer program for the quantitative analysis of EMG signals is then proposed. The resulting program allows the diagnosis of neurogenic and myogenic diseases by analyzing the waveforms of the motor unit potentials (MUP's). It also permits the research of the healthy and disturbed neuromuscular control loop by analyzing the point processes given by the activation of the single motor units.     

Interscale wavelet maximum-a fine to coarse algorithm for wavelet analysis of the EMG interference pattern

A method has been developed, interscale wavelet maximum (ISWM), for characterising the electromyogram (EMG) interference pattern to assist in the diagnosis of neuromuscular disease. EMG signals are decomposed with the redundant dyadic wavelet transform and wavelet maxima (WM) are found. Thresholding methods are applied to remove WM due to noise and background activity. An efficient fine-to-coarse algorithm identifies the WM tree structure for the motor unit action potential rising edges. The WM for each tree are summed at each scale; the largest value is the ISWM. Highly significant differences in ISWM values have been found between healthy, myopathic, and neuropathic subjects that could make the technique a useful diagnostic tool.     

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 Procedure for Decomposing the Myoelectric Signal Into Its Constituent Action Potentials?Part I: Technique, Theory, and Implementation

A technique has been developed which enables the decomposition (separation) of a myoelectric signal into its constituent motor unit action potential trains. It consists of a multichannel (via one electrode) myoelectric signal recording procedure, a data compression algorithm, a digital filtering algorithm, and a hybrid visual-computer decomposition scheme. The algorithms have been implemented on a PDP 11/34 computer. Of the four major segments of the technique, the decomposition scheme is by far the most involved. The decomposition algorithm uses a-sophisticated template matching routine and details of the firing statistics of the motor units to identify motor unit action potentials in the myoelectric signal, even when they are super-imposed with other motor unit action potentials. In general, the algorithms of the decomposition scheme do not run automatically. They require input from the human operator to maintain reliability and accuracy during a decomposition.     

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.     

A cardiac hypothesis for the origin of EEG alpha

Major hypotheses for the origin of EEG alpha suggest a neural basis, but omit to consider other electrical and mechanical aspects. A cardiac electromechanical hypothesis is proposed. This hypothesis suggests that the natural mechanical frequency of the skull-brain mass is approximately 10 Hz. This has been determined by model and direct measurement. Membrane, liquid junction, electrode, and static charges exist as nonneural sources of potential in EEG recording. These standing potentials can be modulated by pressure and movement. It is postulated that the arterial pulse shocks the brain mass into oscillation at its natural frequency modulating the nonneural potentials. The arterial pulse has a jitter as great as ±720 electrical degrees at 10 Hz and amplitude jitter of approximately ±10 mmHg. Because of cardiac jitter, the differential aspect of amplification, and mixing of waves in the skull, coherence of alpha with phase of the cardiac cycle could not be demonstrated. The cardiac electromechanical hypothesis may assist others in designing experiments that will establish a valid explanation for the origin of EEG alpha.     

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.     

Spectral Analysis of Surface Motor Unit Action Potentials and Surface Interference Electromyogram

Surface interference electromyograms (EMG's) were recorded from the tibial muscle of a healthy subject during 50 percent maximal contraction and single motor unit action potentials (MUAP's) were isolated by averaging from the interference pattern. The formation of the EMG was simulated by summing isolated MUAP's according to the statistical properties of the corresponding motor unit discharges. Power spectral density functions (PSDF's) were finally computed for single MUAP's as well as for simulated and experimental EMG's and compared with each other.     

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.     

A Nonstationary Model for the Electromyogram

A theoretical model of the electromyographic (EMG) signal has been developed. In the model, the neural pulse train inputs were considered to be point processes which passed through linear, time-invariant systems that represented the respective motor unit action potential. The outputs were then summed to produce the EMG. It was assumed, that in the production of muscle force, the controlled parameter was the number of active motor units, n(t). The model then showed that the EMG can be represented as an amplitude modulation process of the form EMG = [Kn(t)1/2 w(t) with the stochastic process, w(t), having the spectral and probability characteristics of the EMG during a constant contraction. Various assumptions made in the model development have been verified by experiments.     

Selective activation of small motor axons by quasitrapezoidalcurrent pulses

We have found a method to activate electrically smaller nerve fibers without activating larger fibers in the same nerve trunk. The method takes advantage of the fact that action potentials are blocked with less membrane hyperpolarization in larger fibers than in smaller fibers. In our nerve stimulation system, quasitrapezoidal-shaped current pulses were delivered through a tripolar cuff electrode to effect differential block by membrane hyperpolarization. The quasitrapezoidal-shaped pulses with a square leading edge, a 350 microsecond(s) plateau, and an exponential trailing phase ensured the block of propagating action potentials and prevented the occurrence of anodal break excitation. The tripolar cuff electrode design restricted current flow inside the cuff and thus eliminated the undesired nerve stimulation due to a "virtual cathode." Experiments were performed on 13 cats. The cuff electrode was placed on the medial gastrocnemius nerve. Both compound and single fiber action potentials were recorded from L7 ventral root filaments. The results demonstrated that larger alpha motor axons could be blocked at lower current levels than smaller alpha motor axons, and that all alpha fibers could be blocked at lower current levels than gamma fibers. A statistical analysis indicated that the blocking threshold was correlated with the axonal conduction velocity or fiber diameter. This method could be used in physiological experiments and neural prostheses to achieve a small-to-large recruitment order in motor or sensory systems.     

Gesture-based control and EMG decomposition

This paper presents two probabilistic developments for the use with electromyograms (EMGs). First described is a neuroelectric interface for virtual device control based on gesture recognition. The second development is a Bayesian method for decomposing EMGs into individual motor unit action potentials (MUAPs). This Bayesian decomposition method allows for distinguishing individual muscle groups with the goal of enhancing gesture recognition. All examples presented rely upon sampling EMG data from a subject's forearm. The gesture-based recognition uses pattern recognition software that has been trained to identify gestures from among a given set of gestures. The pattern recognition software consists of hidden Markov models, which are used to recognize the gestures as they are being performed in real time from moving averages of EMGs. Two experiments were conducted to examine the feasibility of this interface technology. The first replicated a virtual joystick interface, and the second replicated a keyboard. Moving averages of EMGs do not provide an easy distinction between fine muscle groups. To better distinguish between different fine motor skill muscle groups, we present a Bayesian algorithm to separate surface EMGs into representative MUAPs. The algorithm is based on differential variable component analysis, which was originally developed for electroencephalograms. The algorithm uses a simple forward model representing a mixture of MUAPs as seen across multiple channels. The parameters of this model are iteratively optimized for each component. Results are presented on both synthetic and experimental EMG data. The synthetic case has additive white noise and is compared with known components. The experimental EMG data were obtained using a custom linear electrode array designed for this study.