Constituent Motor Unit Action Potentials Research Articles

Non-invasive analysis of motor neurons controlling the intrinsic and extrinsic muscles of the hand

Objective. We present a non-invasive framework for investigating efferent commands to 14 extrinsic and intrinsic hand muscles. We extend previous studies (limited to a few muscles) on common synaptic input among pools of motor neurons in a large number of muscles. Approach. Seven subjects performed sinusoidal isometric contractions to complete seven types of grasps, with each finger and with three combinations of fingers in opposition with the thumb. High-density surface EMG (HD-sEMG) signals (384 channels in total) recorded from the 14 muscles were decomposed into the constituent motor unit action potentials. This provided a non-invasive framework for the investigation of motor neuron discharge patterns, muscle coordination and efferent commands of the hand muscles during grasping. Moreover, during grasping tasks, it was possible to identify common neural information among pools of motor neurons innervating the investigated muscles. For this purpose, principal component analysis (PCA) was applied to the smoothed discharge rates of the decoded motor units. Main results. We found that the first principal component (PC1) of the ensemble of decoded motor neuron spike trains explained a variance of (53.0 ± 10.9) % and was positively correlated with force (R = 0.67 ± 0.10 across all subjects and tasks). By grouping the pools of motor neurons from extrinsic or intrinsic muscles, the PC1 explained a proportion of variance of (57.1 ± 11.3) % and (56.9 ± 11.8) %, respectively, and was correlated with force with R = 0.63 ± 0.13 and 0.63 ± 0.13, respectively. Significance. These observations demonstrate a low dimensional control of motor neurons across multiple muscles that can be exploited for extracting control signals in neural interfacing. The proposed framework was designed for hand rehabilitation perspectives, such as post-stroke rehabilitation and hand-exoskeleton control.

ARE MOTOR UNIT FIRING PROPERTIES CONTROLLED WITHIN DISTINCT REGIONS OF A MUSCLE

Previous findings have suggested that the distribution of motor units within a muscle may display region-specific muscle activation. Consequently, if regionalization of motor units indeed exists, then force generating capacities would be highly task and joint dependent. PURPOSE: To examine for regional motor unit control from proximal and distal locations of biarticular [rectus femoris (RF)] and monoarticular [vastus lateralis (VL)] muscles during low-force knee extensions. METHODS: Following 2 maximal voluntary contractions (MVC), eighteen resistance-trained men (n = 9, age = 23 ± 3 yrs) and women (n = 9, 22 ± 2 yrs) performed a 10 sec isometric ramp contraction up to 30% MVC. On two separate occasions, surface electromyographic (EMG) signals were collected from proximal and distal locations of either the VL or RF. These were used to record EMG amplitude and were also decomposed into the constituent motor unit action potentials. The slope and intercept values were calculated across the motor units for relationships between mean firing rate, recruitment threshold, and action potential size for each subject. Paired samples t-tests were used to compare regression coefficients and EMG amplitude between proximal and distal locations of the RF and the VL separately. RESULTS: There were no differences in EMG amplitude between locations in the RF (p = 0.31, d = 0.39), however, amplitude in the distal location of the VL was greater than the proximal (p < 0.05; d = 0.64). There were no significant differences in slope or intercept coefficients for any of the motor unit relationships (see Table 1) (p = 0.08 - 0.91, d = 0.01 - 0.64) CONLUSION: Although there was a regional difference in the activation across the VL, there were no region-specific differences in the motor unit firing properties. The differences in amplitude were likely due to other factors that affect EMG signals, such as the underlying morphology (muscle size, subcutaneous fat thickness, etc.).

How Do the Mechanical Demands of Cycling Affect the Information Content of the EMG?

The persistence of phase-related information in EMG signals can be quantified by its entropic half-life (EnHL). It has been proposed that the EnHL would increase with the demands of a movement task, and thus increase as the pedaling power increased during cycling. However, simulation work on the properties of EMG signals suggests that the EnHL depends on burst duration and duty cycle in the EMG that may not be related to task demands. This study aimed to distinguish between these alternate hypotheses. The EnHL was characterized for 10 muscles from nine cyclists cycling at a range of powers (35 to 260 W) and cadences (60-140 rpm) for the raw EMG, phase-randomized surrogate EMG, EMG intensity, and the principal components describing the muscle coordination patterns. There was phase-related information in the raw EMG signals and EMG intensities that was related to the EMG burst duration, duty cycle pedaling cadence, and power. The EnHL for the EMG intensities of the individual muscles (excluding quadriceps) and for the coordination patterns decreased as cycling power and cadence increased. The EnHL provide information on the structure of the motor control signals and their constituent motor unit action potentials, both within and between muscles, rather than on the mechanical demands of the cycling task per se.

Innervation asymmetry of the external anal sphincter in aging characterized from high-density intra-rectal surface EMG recordings.

The innervation zone asymmetry of the external anal sphincter (EAS) has been investigated as a risk factor for the development of fecal incontinence (FI). This study aims to utilize an intra-rectal high-density surface electromyogram (HD-sEMG) recording and advanced HD-sEMG decomposition technique to characterize the effects of aging on the asymmetry of EAS functional innervation. HD-sEMG signals were recorded intra-rectally from six healthy young and seven healthy elderly women during voluntary contractions of the EAS. EMG signals were decomposed into constituent motor unit action potentials (MUAPs) and the innervation zone of each decomposed motor unit was identified. Asymmetry index (AI) was defined and calculated for all subjects. The maximum squeezing pressures of the EAS were also measured for all subjects as a comparison. The HD-sEMG decomposition and AI calculation were successfully performed from EMG data acquired from all the subjects. The AI values were 28.7 ± 17.0% for the young group and 55.6 ± 18.8% for the elderly group. The AI and EAS contraction strength were found to be negatively correlated (P < 0.05). A two-tailed student's t-test demonstrated a significant increase in AI with age by comparison between two groups (P < 0.05). Our work demonstrates, for the first time, that EAS functional innervation tends to become increasingly asymmetrical with advancing age, and this increase is associated with a compromised anorectal function. Results suggest that the intra-rectal HD-sEMG will serve as an advanced tool for assessing and monitoring the anorectal neuromuscular function minimally invasively under different pathophysiological conditions.

One-Channel Surface Electromyography Decomposition for Muscle Force Estimation.

Estimating muscle force by surface electromyography (sEMG) is a non-invasive and flexible way to diagnose biomechanical diseases and control assistive devices such as prosthetic hands. To estimate muscle force using sEMG, a supervised method is commonly adopted. This requires simultaneous recording of sEMG signals and muscle force measured by additional devices to tune the variables involved. However, recording the muscle force of the lost limb of an amputee is challenging, and the supervised method has limitations in this regard. Although the unsupervised method does not require muscle force recording, it suffers from low accuracy due to a lack of reference data. To achieve accurate and easy estimation of muscle force by the unsupervised method, we propose a decomposition of one-channel sEMG signals into constituent motor unit action potentials (MUAPs) in two steps: (1) learning an orthogonal basis of sEMG signals through reconstruction independent component analysis; (2) extracting spike-like MUAPs from the basis vectors. Nine healthy subjects were recruited to evaluate the accuracy of the proposed approach in estimating muscle force of the biceps brachii. The results demonstrated that the proposed approach based on decomposed MUAPs explains more than 80% of the muscle force variability recorded at an arbitrary force level, while the conventional amplitude-based approach explains only 62.3% of this variability. With the proposed approach, we were also able to achieve grip force control of a prosthetic hand, which is one of the most important clinical applications of the unsupervised method. Experiments on two trans-radial amputees indicated that the proposed approach improves the performance of the prosthetic hand in grasping everyday objects.

A novel method for EMG decomposition based on matched filters

Introduction Decomposition of electromyography (EMG) signals into the constituent motor unit action potentials (MUAPs) can allow for deeper insights into the underlying processes associated with the neuromuscular system. The vast majority of the methods for EMG decomposition found in the literature depend on complex algorithms and specific instrumentation. As an attempt to contribute to solving these issues, we propose a method based on a bank of matched filters for the decomposition of EMG signals. Methods Four main units comprise our method: a bank of matched filters, a peak detector, a motor unit classifier and an overlapping resolution module. The system’s performance was evaluated with simulated and real EMG data. Classification accuracy was measured by comparing the responses of the system with known data from the simulator and with the annotations of a human expert. Results The results show that decomposition of non-overlapping MUAPs can be achieved with up to 99% accuracy for signals with up to 10 active motor units and a signal-to-noise ratio (SNR) of 10 dB. For overlapping MUAPs with up to 10 motor units per signal and a SNR of 20 dB, the technique allows for correct classification of approximately 71% of the MUAPs. The method is capable of processing, decomposing and classifying a 50 ms window of data in less than 5 ms using a standard desktop computer. Conclusion This article contributes to the ongoing research on EMG decomposition by describing a novel technique capable of delivering high rates of success by means of a fast algorithm, suggesting its possible use in future real-time embedded applications, such as myoelectric prostheses control and biofeedback systems.

Decomposition of surface EMG signals from cyclic dynamic contractions.

Over the past 3 decades, various algorithms used to decompose the electromyographic (EMG) signal into its constituent motor unit action potentials (MUAPs) have been reported. All are limited to decomposing EMG signals from isometric contraction. In this report, we describe a successful approach to decomposing the surface EMG (sEMG) signal collected from cyclic (repeated concentric and eccentric) dynamic contractions during flexion/extension of the elbow and during gait. The increased signal complexity introduced by the changing shapes of the MUAPs due to relative movement of the electrodes and the lengthening/shortening of muscle fibers was managed by an incremental approach to enhancing our established algorithm for decomposing sEMG signals obtained from isometric contractions. We used machine-learning algorithms and time-varying MUAP shape discrimination to decompose the sEMG signal from an increasingly challenging sequence of pseudostatic and dynamic contractions. The accuracy of the decomposition results was assessed by two verification methods that have been independently evaluated. The firing instances of the motor units had an accuracy of ∼90% with a MUAP train yield as high as 25. Preliminary observations from the performance of motor units during cyclic contractions indicate that during repetitive dynamic contractions, the control of motor units is governed by the same rules as those evidenced during isometric contractions. Modifications in the control properties of motoneuron firings reported by previous studies were not confirmed. Instead, our data demonstrate that the common drive and hierarchical recruitment of motor units are preserved during concentric and eccentric contractions.

Firing rates of motor units in human vastus lateralis muscle during fatiguing isometric contractions

We investigated the firing rate of motor units in the vastus lateralis muscle in five healthy young men (mean = 21.4 yr, SD = 0.9) during a sequence of isometric constant-torque contractions repeated to exhaustion. The contractions were sustained at 20% of the maximal voluntary level, measured at the beginning of the test sequence. Electromyographic (EMG) signals were recorded via quadrifilar fine-wire electrodes and subsequently decomposed into their constituent motor unit action potentials to obtain the motor unit firing times. In addition, we measured the whole muscle mechanical properties during the fatigue task using electrical stimulation. The firing rate of motor units first decreased within the first 10-20% of the endurance time of the contractions and then increased. The firing rate increase was accompanied by recruitment of additional motor units as the force output remained constant. The elicited twitch and tetanic torque responses first increased and then decreased. The two processes modulated in a complementary fashion at the same time. Our data suggest that, when the vastus lateralis muscle is activated to maintain a constant torque output, its motoneuron pool receives a net excitatory drive that first decreases to compensate for the short-lived potentiation of the muscle force twitch and then increases to compensate for the diminution of the force twitch. The underlying inverse relationship between the firing rate and the recruitment threshold that has been reported for nonfatigued contractions is maintained. We, therefore, conclude that the central nervous system control of vastus lateralis motor units remains invariant during fatigue in submaximal isometric isotonic contractions.

Motor unit estimates obtained using the new "MUESA" method.

Motor unit number estimates were obtained from the extensor digitorum brevis and thenar muscles using a new method called MUESA. MUESA is distinguished from other estimation methods in the manner in which it deals with probabilistic motor unit activation, which is more commonly referred to as "alternation." Because of "alternation," incremental increases in the observed muscle potentials often cannot be interpreted in terms of the successive activation of single motor units. In the MUESA method, the nerve is subjected to a number of constant-intensity stimulus trains, and the resultant muscle response sequences are decomposed into their constituent motor unit action potentials. In general, if a stimulus train results in the probabilistic activation of n motor units, we can expect to see up to 2n different potentials, with each potential representing a unique combination of active and/or inactive motor units. If all 2n potentials are indeed observed, the decomposition of the observed potential sequence into its constituent motor unit action potentials is very straightforward. For the majority of the cases in which the number of observed potentials is not an integer power of 2, we have developed a novel decomposition method based on the analysis of the relative firing rates of the motor units.

Motor unit estimates obtained using the new “MUESA” method

Motor unit number estimates were obtained from the extensor digitorum brevis and thenar muscles using a new method called MUESA. MUESA is distinguished from other estimation methods in the manner in which it deals with probabilistic motor unit activation, which is more commonly referred to as “alternation.” Because of “alternation,” incremental increases in the observed muscle potentials often cannot be interpreted in terms of the successive activation of single motor units. In the MUESA method, the nerve is subjected to a number of constant-intensity stimulus trains, and the resultant muscle response sequences are decomposed into their constituent motor unit action potentials. In general, if a stimulus train results in the probabilistic activation of n motor units, we can expect to see up to 2n different potentials, with each potential representing a unique combination of active and/or inactive motor units. If all 2n potentials are indeed observed, the decomposition of the observed potential sequence into its constituent motor unit action potentials is very straightforward. For the majority of the cases in which the number of observed potentials is not an integer power of 2, we have developed a novel decomposition method based on the analysis of the relative firing rates of the motor units. © 1996 John Wiley & Sons, Inc.

New signal processing techniques for the decomposition of EMG signals.

This paper relates to the use of knowledge-based signal processing techniques in the decomposition of EMG signals. The aim of the research is to automatically decompose EMG signals recorded at force levels up to 20 per cent maximum voluntary contraction (MVC) into their constituent motor unit action potentials (MUAPS), and to display the MUAP shapes and firing times for the clinician. This requires the classification of nonoverlapping MUAPs and superimposed waveforms formed from overlapping MUAPs in the signal. Nonoverlapping MUAPs are classified using a statistical pattern-recognition method. The decomposition of superimposed waveforms uses a combination of procedural and knowledge-based methods. The decomposition method was tested on real and simulated EMG data recorded at force levels up to 20 per cent MVC. The different EMG signals contained up to six motor units (MUs). The new decomposition program classifies the total number of MUAP firings in an EMG signal with an accuracy always greater than 95 per cent. The decomposition program takes about 15s to classify all nonoverlapping MUAPs in EMG signal of length 1.0s and, on average, an extra 9s to classify each superimposed waveform.

Analysis of the neuromuscular system using random parameter models

Noninvasive procedures for studies of physiological systems using real-time experimental data have attracted considerable attention from researchers recently. The primary objective is to avoid measurements of the system input signals, which can generally be obtained only by invasive clinical methods. It is envisaged that system-theoretic fundamentals along with techniques of system identification and estimation may be utilized effectively to achieve this goal. In the analysis of the neuromuscular system (NMS), the following factors are involved: (a) assumptions about the possible stochastic features of the input, categorizing them into various classes for test purposes; (b) specification of the system structure using the actual anatomical and physiological information, and the stochastic nature of the NMS output; and (c) application of a class of multipath random models is proposed. A statistical approach is adopted for analyzing the multipath model to estimate the probable behavior of the NMS. The proposed multipath model contains in each path a realization of a random parameter model which is a cascade of random nonlinear and linear blocks. This approach shows that the multipath models with nonlinearities in each path, have an expected output that is linearly related to the input under certain conditions. This approach aims to decompose the clinical electromyograms into their constituent motor-unit action potentials and their configurational and firing properties.

AAEE minimonograph #29: automatic quantitative electromyography.

The present status of different computerized methods of automatic quantitative electromyography are reviewed. Interference pattern methods-turns analysis, spectral analysis-are efficient, but the results usually cannot be directly related to the physiological properties of the motor units. Integration analysis does not currently have a major role in diagnostic electromyography. Traditional measurement of single motor unit action potentials during weak contraction can be facilitated and made more objective with computer assistance, but only the lowest-threshold motor units in the muscle are amenable to study. A new class of methodologies under development permit the decomposition of interference patterns into their constituent motor unit action potentials for measurement of configurational and behavioral properties. Patient data from these various methods can be statistically compared with normative data bases available on-line in computerized electromyographs. Both quantitative and quantitative electromyography have applications in the neuromuscular electrodiagnostic examination.

Changes of the average muscle fiber conduction velocity during a varying force contraction

The average conduction velocity of muscle fibers measured with a surface electrode has been reported to increase with the level of contraction force. To clarify the factors causing this increase, the authors decomposed the interference surface EMG signal into its constituent motor unit action potentials and compared the average conduction velocity with the conduction velocity measured at the individual discharges of motor units. The conduction velocity within individual motor units increased with the contraction force and contributed to the increase of the average conduction velocity. However, the increase of the conduction velocity within motor units was relatively small and was insufficient to cause the increase of the average conduction velocity. This result indicates that the motor units recruited at higher thresholds of force should have higher conduction velocities.

Automatic decomposition electromyography (ADEMG): validation and normative data in brachial biceps

We describe a new, automatic method (ADEMG) for decomposing EMG interference patterns into their constituent motor unit action potentials (MUAPs), and quantitating the configurational and firing properties of the MUAPs. ADEMG is fast (90 sec analysis time for a 10 sec EMG epoch) and efficient (33–98% of MUAP occurences correctly identified) because of 4 signal-processing innovations designated digital prefiltering, high-resolution wave from alignment, firing-time analysis and interference-cancellation averaging. Validation experiments are described involving recruitment/derecruitment of low-threshold MUAPs, and single-fiber-triggered averaging. Normative data are presented for 2000 MUAPs from brachial biceps (mean 9.7 MUAPs per site) at 3 levels of isometric contraction (7%, 15% and 30% MVC) and contrasted with normal findings obtained using traditional, low-threshold MUAP analysis. The main advantages of ADEMG are (1) speed of data acquisition and processing in the clinical setting; (2) ability to analyze both low-and high-threshold MUAPs during moderately strong muscular contractions; and (3) MUAP firing-rate information.