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Recent advances in high-density surface electromyogram (EMG) decomposition have made it a feasible task to discriminate single motor unit activity from surface EMG interference patterns, thus providing a noninvasive approach for examination of motor unit control properties. In the current study, we applied high-density surface EMG recording and decomposition techniques to assess motor unit firing behavior alterations poststroke. Surface EMG signals were collected using a 64-channel 2-D electrode array from the paretic and contralateral first dorsal interosseous (FDI) muscles of nine hemiparetic stroke subjects at different isometric discrete contraction levels between 2 to 10 N with a 2 N increment step. Motor unit firing rates were extracted through decomposition of the high-density surface EMG signals and compared between paretic and contralateral muscles. Across the nine tested subjects, paretic FDI muscles showed decreased motor unit firing rates compared with contralateral muscles at different contraction levels. Regression analysis indicated a linear relation between the mean motor unit firing rate and the muscle contraction level for both paretic and contralateral muscles (p < 0.001), with the former demonstrating a lower increment rate (0.32 pulses per second (pps)/N) compared with the latter (0.67 pps/N). The coefficient of variation (averaged over the contraction levels) of the motor unit firing rates for the paretic muscles (0.21 ± 0.012) was significantly higher than for the contralateral muscles (0.17 ± 0.014) (p < 0.05). This study provides direct evidence of motor unit firing behavior alterations poststroke using surface EMG, which can be an important factor contributing to hemiparetic muscle weakness.

A surface EMG signal represents the linear transformation of motor neuron discharge times by the compound action potentials of the innervated muscle fibers and is often used as a source of information about neural activation of muscle. However, retrieving the embedded neural code from a surface EMG signal is extremely challenging. Most studies use indirect approaches in which selected features of the signal are interpreted as indicating certain characteristics of the neural code. These indirect associations are constrained by limitations that have been detailed previously (Farina D, Merletti R, Enoka RM. J Appl Physiol 96: 1486-1495, 2004) and are generally difficult to overcome. In an update on these issues, the current review extends the discussion to EMG-based coherence methods for assessing neural connectivity. We focus first on EMG amplitude cancellation, which intrinsically limits the association between EMG amplitude and the intensity of the neural activation and then discuss the limitations of coherence methods (EEG-EMG, EMG-EMG) as a way to assess the strength of the transmission of synaptic inputs into trains of motor unit action potentials. The debated influence of rectification on EMG spectral analysis and coherence measures is also discussed. Alternatively, there have been a number of attempts to identify the neural information directly by decomposing surface EMG signals into the discharge times of motor unit action potentials. The application of this approach is extremely powerful, but validation remains a central issue., (Copyright © 2014 the American Physiological Society.)

Introduction and Hypothesis: Locating the innervation zones (IZs) of the external anal sphincter (EAS) is helpful to obstetricians to identify areas particularly vulnerable to episiotomy in pregnant women. The aim was to investigate the motor unit (MU) properties of the EAS during voluntary contractions., Methods: Electromyographic signals were detected, from 478 pregnant women, by means of an intra-anal cylindrical probe carrying a circumferential array of 16 electrodes. The signals were decomposed into the constituent MU action potential trains and 5,947 templates were extracted and analyzed in order to identify the IZ position., Results: MUs innervated at one end are concentrated in the dorsal portion of the sphincter, while MUs innervated in the middle are distributed symmetrically in the left and right portions of the EAS. The angular propagation velocity was estimated for each MU resulting in 260 ± 45 rad/s, corresponding to 1.8 m/s on the probe surface and to about 4 m/s at a radial depth of 10 mm from the probe surface., Conclusions: A novel method for identification and classification of MUs of the EAS is proposed and applied to a large-scale study. It is possible to distinguish MUs of the EAS in a minimally invasive way and identify their IZs. This information should be used to plan episiotomies and minimize risks of EAS denervation.

Muscle fiber conduction velocity (CV) is commonly estimated from surface electromyograms (EMGs) collected with electrodes parallel to muscle fibers. If electrodes and muscle fibers are not located in parallel planes, CV estimates are biased towards values far over the physiological range. In virtue of their pinnate architecture, the fibers of muscles such as the gastrocnemius are hardly aligned in planes parallel to surface electrodes. Therefore, in this study we investigate whether physiological CV estimates can be obtained from the gastrocnemius muscle. Specifically, with a large grid of 16×8 electrodes we map CV estimates over the whole gastrocnemius muscle while eleven subjects exerted isometric plantar flexions at three different force levels. CV was estimated for couples of single differential EMGs and estimate locations (i.e., channels) were classified as physiological and non-physiological, depending on whether CV estimates were within the physiological range (3-6ms(-1)) or not. Physiological CV values could be estimated from a markedly small muscle region for eight participants; channels providing physiological CV estimates corresponded to about 5% of the total number of channels. As expected, physiological and non-physiological channels were clustered in distinct regions. CV estimates within the physiological range were obtained for the most distal gastrocnemius portion (ANOVA, P<0.001), where occurrences of propagating potentials were often verified through visual analysis. For the first time, this study shows that CV might be reliably assessed from surface EMGs collected from the most distal gastrocnemius region., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

The recruitment and the rate of discharge of motor units are determinants of muscle force. Within a motoneuron pool, recruitment and rate coding of individual motor units might be controlled independently, depending on the circumstances. In this study, we tested whether, during human quiet standing, the force of the medial gastrocnemius (MG) muscle is predominantly controlled by recruitment or rate coding. If MG control during standing was mainly due to recruitment, then we further asked what the trigger mechanism is. Is it determined internally, or is it related to body kinematics? While seven healthy subjects stood quietly, intramuscular electromyograms were recorded from the MG muscle with three pairs of wire electrodes. The number of active motor units and their mean discharge rate were compared for different sway velocities and positions. Motor unit discharges occurred more frequently when the body swayed faster and forward (Pearson R = 0.63; P < 0.0001). This higher likelihood of observing motor unit potentials was explained chiefly by the recruitment of additional units. During forward body shifts, the median number of units detected increased from 3 to 11 (P < 0.0001), whereas the discharge rate changed from 8 ± 1.1 (mean ± SD) to 10 ± 0.9 pulses/s (P = 0.001). Strikingly, motor units did not discharge continuously throughout standing. They were recruited within individual, forward sways and intermittently, with a modal rate of two recruitments per second. This modal rate is consistent with previous circumstantial evidence relating the control of standing to an intrinsic, higher level planning process.

Electrical stimulation of innervated muscles has been investigated for many decades with alternations of high and low clinical interest in the fields of rehabilitation medicine and sports sciences. Early work demonstrated that afferent fibers have lower thresholds and are usually activated first (therefore eliciting an H-reflex). In the case of nerve trunk stimulation, the order of recruitment is mostly conditioned by the axonal dimension and excitability threshold. In the case of muscle motor point stimulation, the spatial distribution of nerve branches plays a predominant role. Sustained stimulation produces a progressive increase of force that is often maintained in subsequent voluntary activation by stroke patients. This observation suggested a facilitation mechanism at the spinal and/or supraspinal level. Such facilitation has been observed in healthy subjects as well, and may explain the generation of cramps elicited during stimulation and sustained for dozens of seconds after the stimulation has been interrupted. The most recent interpretations of facilitation resulting from peripheral stimulation focused on presynaptic (potentiation of neurotransmitter release from afferent fibers) or postsynaptic (generation of "persistent inward currents" in spinal motor neurons or interneurons) mechanisms. The renewed attention to these phenomena is once more increasing the interest toward electrical stimulation of the neuromuscular system. This is an opportunity for a structured investigation of the field aimed to resolving elements of confusion and controversy that still plague this area of electrophysiology., (© 2011, Copyright the Authors. Artificial Organs © 2011, International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.)

In cat medial gastrocnemius (MG), fibres supplied by individual motoneurones (muscle units) distribute extensively along the muscle longitudinal axis. In the human MG, the size of motor unit territory is unknown. It is uncertain if the absolute size of muscle unit territory or the size relative to the whole muscle is most comparable with the cat. By comparing intramuscular and surface electromyograms we tested whether muscle units extend narrowly or widely along the human MG muscle. Due to the pennation of the MG, if individual motoneurones supply fibres scattered along the muscle, then action potentials of single motor units are expected to appear sparsely on the surface of the skin. In nine healthy subjects, pairs of wire electrodes were inserted in three locations along the MG muscle (MG60%, MG75% and MG90%). A longitudinal array of 16 surface electrodes was positioned alongside the intramuscular electrodes. While subjects stood quietly, 55 motor units were identified, of which, significantly more units were detected in the most distal sites. The surface action potentials had maximum amplitude at 4.40 ±1.67 (mean±S.D.), 8.02±2.16 and 11.63±2.09 cm (P <0.001) from the most proximal surface electrode, for motor units in the MG60%, MG75% and MG90% locations, respectively. Single motor unit potentials were recorded by five consecutive surface electrodes, at most, indicating that muscle units extend shortly along the MG longitudinal axis. It is concluded that relative to the whole muscle, and compared with the cat, muscle units in human MG are localised. The localisation of muscle units might have implications for the regional control of muscle activity.

The aim of this study was to investigate changes in experimentally recorded M-waves with progressive motor unit (MU) activation induced by transcutaneous electrical stimulation with different pulse waveforms. In 10 subjects, surface electromyographic signals were detected with a linear electrode array during electrically elicited contractions of the biceps brachii muscle. Three different monophasic waveforms of 304-micros duration were applied to the stimulation electrode on the main muscle motor point: triangular, square, and sinusoidal. For each waveform, increasing stimulation current intensities were applied in 10 s (frequency: 20 Hz). It was found that: (a) the degree of MU activation, as indicated by M-wave average rectified value, was a function of the injected charge and not of the stimulation waveform, and (b) MUs tended to be recruited in order of increasing conduction velocity with increasing charge of transcutaneous stimulation. Moreover, the subjects reported lower discomfort during the contractions elicited by the triangular waveform with respect to the others. Since subject tolerance to the stimulation protocol must be considered as important as MU recruitment in determining the effectiveness of neuromuscular electrical stimulation (NMES), we suggest that both charge and waveform of the stimulation pulses should be considered relevant parameters for optimizing NMES protocols.

Objective: We systematically tested the capability of the Convolution Kernel Compensation (CKC) method to identify motor unit (MU) discharge patterns from the simulated and experimental surface electromyogram (sEMG) during low-force contractions., Methods: sEMG was detected with a grid of 13 x 5 electrodes. In simulated signals with 20 dB signal-to-noise ratio, 11+/-3 out of 63 concurrently active MUs were identified with sensitivity >95% in the estimation of their discharge times. In experimental signals recorded at 0-10% of the maximal force, the discharge patterns of (range) 11-19 MUs (abductor pollicis; n=8 subjects), 9-17 MUs (biceps brachii; n=2), 7-11 MUs (upper trapezius; n=2), and 6-10 MUs (vastus lateralis; n=2) were identified. In the abductor digiti minimi muscle of one subject, the decomposition results from concurrently recorded sEMG and intramuscular EMG (iEMG) were compared; the two approaches agreed on 98+/-1% of MU discharges., Conclusion: It is possible to identify the discharge patterns of several MUs during low-force contractions from high-density sEMG., Significance: sEMG can be used for the analysis of individual MUs when the application of needles is not desirable or in combination with iEMG to increase the number of sampled MUs.

We investigated the changes in muscle fiber conduction velocity, recruitment and derecruitment thresholds, and discharge rate of low-threshold motor units during a series of ramp contractions. The aim was to compare the adjustments in motor unit activity relative to the duration that each motor unit was active during the task. Multichannel surface electromyographic (EMG) signals were recorded from the abductor pollicis brevis muscle of eight healthy men during 12-s contractions (n = 25) in which the force increased and decreased linearly from 0 to 10% of the maximum. The maximal force exhibited a modest decline (8.5 +/- 9.3%; P < 0.05) at the end of the task. The discharge times of 73 motor units that were active for 16-98% of the time during the first five contractions were identified throughout the task by decomposition of the EMG signals. Action potential conduction velocity decreased during the task by a greater amount for motor units that were initially active for >70% of the time compared with that of less active motor units. Moreover, recruitment and derecruitment thresholds increased for these most active motor units, whereas the thresholds decreased for the less active motor units. Another 18 motor units were recruited at an average of 171 +/- 32 s after the beginning of the task. The recruitment and derecruitment thresholds of these units decreased during the task, but muscle fiber conduction velocity did not change. These results indicate that low-threshold motor units exhibit individual adjustments in muscle fiber conduction velocity and motor neuron activation that depended on the relative duration of activity during intermittent contractions.

Although the behaviour of individual motor units is classically studied with intramuscular EMG, recently developed techniques allow its analysis also from EMG recorded in multiple locations over the skin surface (high-density surface EMG). The analysis of motor units from the surface EMG is useful when the insertion of needles is not desirable or not possible. Moreover, surface EMG allows the measure of motor unit properties which are difficult to assess with invasive technology (e.g., muscle fiber conduction velocity or location of innervation zones) and may increase the number of detectable motor units with respect to selective intramuscular recordings. Although some limitations remain, both the discharge pattern and muscle fiber properties of individual motor units can currently be analyzed non-invasively. This review presents the conditions and methodologies which allow the investigation of motor units with surface EMG.

Purpose: The purpose of the study was to demonstrate that anatomical features of individual motor units of the puborectalis muscle can be detected with non-invasive electromyography (EMG) and to evaluate differences in electrophysiological properties of the puborectalis muscles in a small group of healthy and pathologic subjects., Methods: Multichannel EMG was recorded by means of a flexible probe applied on the gloved index finger and carrying an array of eight equally spaced (1.15 mm) electrodes. A multichannel EMG amplifier provided seven outputs corresponding to the pairs of adjacent electrodes. Tests were performed in three different positions (dorsal, left and right) over the puborectalis muscle on 20 subjects (nine healthy, seven constipated and four incontinent patients). Motor unit action potentials (MUAPs) generated at the innervation zone of a MU and propagating along the muscle fibers generated repetitive characteristic patterns on the seven output channels allowing identification of anatomical features of the motor units., Results: MUAPs were observed travelling in either one or both directions with the array in dorsal position, and mainly in ventral-to-dorsal direction in either lateral position. MUAP amplitude was lower in constipated and incontinent patients with respect to healthy subjects. The conduction velocity estimated on the identified MUAPs was lower for constipated patients with respect to healthy subjects suggesting different mechanical properties of the active motor units., Conclusions: This technique allows the extraction of relevant information about the anatomical features (innervation zone position and overlapping of motor unit branches) of the puborectalis muscle and its electrophysiological properties and maybe can be applied as an novel methodology for assessing the anorectal function in patients.

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.

The purpose of the study was to evaluate the use of cross-correlation analysis between simulated surface electromyograms (EMGs) of two muscles to quantify motor unit synchronization. The volume conductor simulated a cylindrical limb with two muscles and bone, fat, and skin tissues. Models of two motor neuron pools were used to simulate 120 s of surface EMG that were detected over both muscles. Short-term synchrony was established using a phenomenological model that aligned the discharge times of selected motor units within and across muscles to simulate physiological levels of motor unit synchrony. The correlation between pairs of surface EMGs was estimated as the maximum of the normalized cross-correlation function. After imposing four levels of motor unit synchrony across muscles, five parameters were varied concurrently in the two muscles to examine their influence on the correlation between the surface EMGs: 1) excitation level (5, 10, 15, and 50% of maximum); 2) muscle size (350 and 500 motor units); 3) fat thickness (1 and 4 mm); 4) skin conductivity (0.1 and 1 S/m); and 5) mean motor unit conduction velocity (2.5 and 4 m/s). Despite a constant and high level of motor unit synchronization among pairs of motor units across the two muscles, the cross-correlation index ranged from 0.08 to 0.56, with variation in the five parameters. For example, cross-correlation of EMGs from pairs of hand muscles, each having thin layers of subcutaneous fat and mean motor unit conduction velocities of 4 m/s, may be relatively insensitive to the level of synchronization across muscles. In contrast, cross-correlation of EMGs from pairs of leg muscles, with larger fat thickness, may exhibit a different sensitivity. These results indicate that cross correlation of the surface EMGs from two muscles provides a limited measure of the level of synchronization between motor units in the two muscles.

In order to better understand the mechanisms of generation of mechanomyography (MMG) signals, the two-dimensional distribution of surface MMG produced by the activity of single motor units was analyzed by a novel two-dimensional recording method. Motor unit action potentials were identified from intramuscular electromyographic (EMG) signals and used to trigger the averaging of MMG signals detected over the tibialis anterior muscle of 11 volunteers with a grid of 5x3 accelerometers (20-mm inter-accelerometer distance). The intramuscular wires were inserted between the first and second accelerometer in the middle column of the grid, proximal to the innervation zone. The subjects performed three contractions with visual feedback of the intramuscular EMG signals. In each contraction, a new motor unit was recruited at the minimum stable discharge rate (mean+/-S.D., N = 11 subjects, 7.3+/-2.3 pulse/s), resulting in torque of 2.4+/-2.8% of the maximal voluntary contraction (MVC), 4.6+/-2.7% MVC, and 6.3+/-3.1% MVC (all different, P < 0.01). For 23 out of 33 detected motor units, it was possible to extract the motor unit surface acceleration map (MUAM). A negative MUAM peak (-2.7+/-2.2 mm/s2) was detected laterally and a positive MUAM peak (4.1+/-2.4 mm/s2) medially (P < 0.001). The time-to-peak was shorter in the medial part of the muscle (2.9+/-0.4 ms) than in the other locations (3.4+/-0.5 ms, P < 0.001). The double integrated signals (muscle displacement) indicated negative deflection in the lateral part and inflation close to the tibia bone. The maps of acceleration showed spatial dependency in single motor unit MMG activities. The technique provides a new insight into motor unit contractile properties.

The purpose of the study was to evaluate the influence of selected physiological parameters on amplitude cancellation in the simulated surface electromyogram (EMG) and the consequences for spike-triggered averages of motor unit potentials derived from the interference and rectified EMG signals. The surface EMG was simulated from prescribed recruitment and rate coding characteristics of a motor unit population. The potentials of the motor units were detected on the skin over a hand muscle with a bipolar electrode configuration. Averages derived from the EMG signal were generated using the discharge times for each of the 24 motor units with lowest recruitment thresholds from a population of 120 across three conditions: 1) excitation level; 2) motor unit conduction velocity; and 3) motor unit synchronization. The area of the surface-detected potential was compared with potentials averaged from the interference, rectified, and no-cancellation EMGs. The no-cancellation EMG comprised motor unit potentials that were rectified before they were summed, thereby preventing cancellation between the opposite phases of the potentials. The percent decrease in area of potentials extracted from the rectified EMG was linearly related to the amount of amplitude cancellation in the interference EMG signal, with the amount of cancellation influenced by variation in excitation level and motor unit conduction velocity. Motor unit synchronization increased potentials derived from both the rectified and interference EMG signals, although cancellation limited the increase in area for both potentials. These findings document the influence of amplitude cancellation on motor unit potentials averaged from the surface EMG and the consequences for using the procedure to characterize motor unit properties.

The aim of the study was to investigate amplitude and frequency content of single motor unit (MU) electromyographic (EMG) and mechanomyographic (MMG) responses. Multi-channel surface EMG and MMG signals were detected from the dominant biceps brachii muscle of 10 volunteers during isometric voluntary contractions at 20%, 50%, and 80% of the maximal voluntary contraction (MVC) force. Each contraction was performed three times in the experimental session which was repeated in three non-consecutive days. Single MU action potentials were identified from the surface EMG signals and their times of occurrence used to trigger the averaging of the MMG signal. At each contraction level, the MUs with action potentials of highest amplitude were identified. Single MU EMG and MMG amplitude and mean frequency were estimated with normalized standard error of the mean within subjects (due to repetition of the measure in different trials and experimental sessions) smaller than 15% and 7%, respectively, in all conditions. The amplitude of the action potentials of the detected MUs increased with increasing force (mean +/- SD, 244 +/- 116 microV at 20% MVC, and 1426 +/- 638 microV at 80% MVC; P < 0.001) while MU MMG amplitude increased from 20% to 50% MVC (40.5 +/- 20.9 and 150 +/- 88.4 mm/s(2), respectively; P<0.001) and did not change significantly between 50% and 80% MVC (129 +/ -82.7 mm/s(2) at 80% MVC). MU EMG mean frequency decreased with contraction level (20% MVC: 97.2 +/- 13.9 Hz; 80% MVC: 86.2 +/- 11.4 Hz; P < 0.001) while MU MMG mean frequency increased (20% MVC: 33.2 +/- 6.8 Hz; 80% MVC: 40.1 +/- 6.1 Hz; P < 0.001). EMG peak-to-peak amplitude and mean frequency of individual MUs were not correlated with the corresponding variables of MMG at any contraction level.

The estimation of fibre length in jaw-elevator muscles is important for modelling studies and clinical applications. The objective of this study was to identify, from multi-channel surface EMG recordings, the main innervation zone(s) of the superficial masseter and anterior temporalis muscles, and to estimate the fibre length of these muscles. Surface EMG signals were collected from 13 subjects with a 16-electrode linear array. The innervation zones of the masseter and anterior temporalis were identified and their variability intra- and inter-subject outlined. More than one main innervation zone location was identified in the masseter of all subjects and in the temporalis anterior of 12 subjects. Average estimated fibre lengths, for the right (left) side, were (mean+/-SD) 27.3+/-2.4 mm (27.0+/-1.7 mm) and 25.9+/-2.3 mm (26.6+/-1.6 mm), for the superficial masseter and temporalis anterior muscle, respectively. The range of innervation zone locations was up to approximately 50% of the fibre length, both within and between subjects. Fibre length estimates well matched with published data on cadavers. It was concluded that multi-channel surface EMG provides important and reliable information on the anatomy of single motor units in jaw-elevator muscles.

The aim of this study was to interpret changes in experimentally recorded M waves with progressive motor unit (MU) activation based on simulation of the surface electromyogram. Activation order during transcutaneous electrical stimulation was analyzed by investigating M-wave average rectified value, spectral properties, and conduction velocity (CV) during electrically elicited contractions. M-waves were detected from the biceps brachii muscle of 10 healthy male subjects by a linear adhesive array of eight electrodes. Electrical stimulation was delivered to the motor point at either constant current intensity (40, 60, 80, and 100% of the supramaximal stimulation current) or with linearly increasing current. A model of surface electromyogram generation that varied activation order based on MU size and location was used to interpret the experimental results. From the experimental and model analysis, it was found that 1) MUs tended to be activated from low to high CV and from the superficial to the deep muscle layers with increasing transcutaneous electrical stimulation of the biceps brachii muscle, and 2) characteristic spectral frequencies of the M-wave were affected by many factors other than average CV (such as the activation order by MU location or the spread of the MU innervation zones and CVs), thus decreasing with a concomitant increase in CV during progressive MU activation.

It has been shown that multi-channel surface EMG allows assessment of anatomical and physiological single motor unit (MU) properties. To get this information, the action potentials of single MUs should be extracted from the interference EMG signals. This study describes an automatic system for the detection and classification of MU action potentials from multi-channel surface EMG signals. The methods for the identification and extraction of action potentials from the raw signals and for their clustering into the MUs to which they belong are described. The segmentation phase is based on the matched Continuous Wavelet Transform (CWT) while the classification is performed by a multi-channel neural network that is a modified version of the multi-channel Adaptive Resonance Theory networks. The neural network can adapt to slow changes in the shape of the MU action potentials. The method does not require any interaction of the operator. The technique proposed was validated on simulated signals, at different levels of force, generated by a structure based surface EMG model. The MUs identified from the simulated signals covered almost the entire recruitment curve. Thus, the proposed algorithm was able to identify a MU sample representative of the muscle. Results on experimental signals recorded from different muscles and conditions are reported, showing the possibility of investigating anatomical and physiological properties of the detected MUs in a variety of practical cases. The main limitation of the approach is that complete firing patterns can be obtained only in specific cases due to MU action potential superpositions., (Copyright 2004 Elsevier B.V.)

The aim of this human study was to investigate the relationship between experimentally induced muscle pain intensity (i.e., amount of nociceptive activity) and motor unit (MU) firing decrease and MU conduction velocity (CV). In 12 healthy subjects, nociceptive afferents were stimulated in the right tibialis anterior muscle by three intramuscular injections of hypertonic saline (0.2, 0.5, and 0.9 ml) separated by 140 s. The subjects performed six isometric contractions (20 s long) at 10% of the maximal voluntary contraction during the experimental muscle pain. The same set of six contractions was performed without any infusion before the painful condition on the right leg. The procedure was repeated for the left leg with infusion of isotonic (nonpainful) saline. Intramuscular and surface electromyographic (EMG) signals were collected to assess MU firing rate and CV. The firing rate of the active MUs [range: 7.4-14.8 pulses/s (pps)] did not change significantly in the three control conditions (without infusion for the right and left leg and with infusion of isotonic saline in the left leg). There was, on the contrary, a significant decrease (on average, mean +/- SE, 1.03 +/- 0.21 pps) of the firing rates during the painful condition. Moreover, MU firing rates were inversely significantly correlated with the subjective scores of pain intensity. Single MU CV was 3.88 +/- 0.03 m/s (mean +/- SE, over all the MUs) with no statistical difference among any condition, i.e., the injection of hypertonic saline did not alter the muscle fiber membrane properties of the observed MUs. Progressively increased muscle pain intensity causes a gradual decrease of MU firing rates. This decrease is not associated with a change in MU membrane properties, indirectly assessed by CV. This study demonstrates a central inhibitory motor control mechanism with an efficacy correlated to the nociceptive activity.

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.

The aim of the study was to compare experimentally, on the basis of single motor unit (MU) activities, the selectivity of different spatial filters commonly used to detect surface electromyogram (EMG) signals. Surface EMG signals were recorded from the biceps brachii and the upper trapezius muscle of five subjects using a two-dimensional (2D) electrode array consisting of 16 pin electrodes. The subjects performed isometric contractions at different elbow angles and shoulder abduction and flexion. The same monopolar surface EMG signals were filtered using longitudinal single and double differential, transverse single and double differential and normal double differential filters. From the single MU action potentials, extracted by automatic EMG decomposition, indexes of transverse (perpendicular with respect to the fibre direction) and longitudinal (along the fibre direction) selectivity were computed. The number of detected MUs was 46 for the upper trapezius, with the arms held in the sagittal plane, and 52 when the arms were held in the frontal plane; 85 MUs were identified from the biceps brachii contractions. The results showed that transverse selectivity was significantly higher for the 2D and transverse one-dimensional (1D) filters with respect to the 1D longitudinal filters, whereas longitudinal selectivity was higher (i.e. MU action potentials were shorter) for the 2D filter and the longitudinal double differential filter. In particular, the relative attenuation of potential amplitude moving 5 mm from the source was, on average (for the two muscles), 16.5% for the least selective filter in the transverse direction (longitudinal single differential) and 35.7% for the most selective one in the same direction (transverse double differential). The MU action potential duration was, on average, 13.8 ms for the most selective filter in the longitudinal direction (longitudinal double differential) and 18.7 ms for the least selective one (transverse double differential). The normal double differential filter resulted in spatial selectivity indexes that ware not statistically different in the two directions from those of the best filters in each direction.

The aim of the study was to compare experimentally conduction velocity (CV) estimates obtained with different estimation methods based on surface electromyogram (EMG) signals detected using five spatial filters. The filters investigated were the longitudinal single and double differential, transverse single and double differential, and normal double differential. The same surface EMG signals detected as described in Part 1 were used in this work. CV was estimated with four commonly used delay estimation techniques, i.e. from the distance between the peak values of two waveforms (with and without polynomial interpolation around the peak), and by the maximum likelihood estimate (MLE) based on two or more surface EMG channels. The average standard deviation of CV estimation (for all the MUs and the two muscles together) was 0.61 m s(-1) and 0.79 m s(-1) for the peak method, with and without interpolation, respectively, and 0.50 m s(-1) and 0.31 m s(-1) for the MLE method, from two and more surface EMG channels, respectively. Moreover, the mean of CV estimates varied by as much as 1 m s(-1) depending on the spatial filter used and the method adopted for CV estimation. Considering the dependence on the spatial filter only, the average (over all estimation methods) CV estimates obtained with the five spatial filters were 4.32 m s(-1) (normal double differential), 4.23 m s(-1) (longitudinal double differential), 4.61 m s(-1) (transverse double differential), 4.64 m s(-1) (transverse single differential) and 4.03 m s(-1) (longitudinal single differential). It was concluded that the comparison of single MU CV values obtained in different studies is critical if different spatial filters and processing techniques are used for their estimation. Higher estimates of CV were attributed to a smaller reduction in non-travelling signal components and thus were assumed to be positively biased.

In this review we describe the basic principles of operation of linear electrode arrays for the detection of surface EMG signals, together with their most relevant current applications. A linear array of electrodes is a system which detects surface EMG signals in a number of points located along a line. A spatial filter is usually placed in each point for signal detection, so that the recording of EMG signals with linear arrays corresponds to the sampling in one spatial direction of a spatially filtered version of the potential distribution over the skin. Linear arrays provide indications on motor unit (MU) anatomical properties, such as the locations of the innervation zones and tendons, and the fiber length. Such systems allow the investigation of the properties of the volume conductor and its effect on surface detected signals. Moreover, linear arrays allow to estimate muscle fiber conduction velocity with a very low standard deviation of estimation (of the order of 0.1-0.2 m/s), thus providing reliable indications on muscle fiber membrane properties and their changes in time (for example with fatigue or during treatment). Conduction velocity can be estimated from a signal epoch (global estimate) or at the single MU level. In the latter case, MU action potentials are identified from the interference EMG signals and conduction velocity is estimated for each detected potential. In this way it is possible, in certain conditions, to investigate single MU control and conduction properties with a completely non-invasive approach. Linear arrays provide valuable information on the neuromuscular system properties and appear to be promising tools for applied studies and clinical research.

During isometric contractions of increasing strength, motor units (MUs) are recruited by the central nervous system in an orderly manner starting with the smallest, with muscle fibers that usually show the lowest conduction velocity (CV). Theory predicts that the higher the velocity of propagation of the action potential, the higher the power at high frequencies of the detected surface signal. These considerations suggest that the power spectral density of the surface detected electromyogram (EMG) signal may give indications about the MU recruitment process. The purpose of this paper is to investigate the potential and limitations of spectral analysis of the surface EMG signal as a technique for the investigation of muscle force control. The study is based on a simulation approach and on an experimental investigation of the properties of surface EMG signals detected from the biceps brachii during isometric linearly increasing torque contractions. Both simulation and experimental data indicate that volume conductor properties play an important role as confounding factors that may mask any relation between EMG spectral variables and estimated CV as a size principle parameter during ramp contractions. The correlation between spectral variables and CV is thus significantly lower when the MU pool is not stable than during constant-torque isometric contractions. Our results do not support the establishment of a general relationship between spectral EMG variables and torque or recruitment strategy.

We propose and test a tool to evaluate and compare EMG signal decomposition algorithms. A model for the generation of synthetic intra-muscular EMG signals, previously described, has been used to obtain reference decomposition results. In order to evaluate the performance of decomposition algorithms it is necessary to define indexes which give a compact but complete indication about the quality of the decomposition. The indexes given by traditional detection theory are in this paper adapted to the multi-class EMG problem. Moreover, indexes related to model parameters are also introduced. It is possible in this way to compare the sensitivity of an algorithm to different signal features. An example application of the technique is presented by comparing the results obtained from a set of synthetic signals decomposed by expert operators having no information about the signal features using two different algorithms. The technique seems to be appropriate for evaluating decomposition performance and constitutes a useful tool for EMG signal researchers to identify the algorithm most appropriate for their needs.

This work addresses the problem of estimating the conduction velocity (CV) of single motor unit (MU) action potentials from surface EMG signals detected with linear electrode arrays during voluntary muscle contractions. In ideal conditions, that is without shape or scale changes of the propagating signals and with additive white Gaussian noise, the maximum likelihood (ML) is the optimum estimator of delay. Nevertheless, other methods with computational advantages can be proposed; among them, a modified version of the beamforming algorithm is presented and compared with the ML estimator. In real cases, the resolution in delay estimation in the time domain is limited because of the sampling process. Transformation to the frequency domain allows a continuous estimation. A fast, high-resolution implementation of the presented multichannel techniques in the frequency domain is proposed. This approach is affected by a negligible decrease in performance with respect to ideal interpolation. Application of the ML estimator, based on two-channel information, to ten firings of each of three MUs provides a CV estimate affected by a standard deviation of 0.5 m s(-1); the modified beamforming and ML estimators based on five channels provide a CV standard deviation of less than 0.1 m s(-1) and allow the detection of statistically significant differences between the CVs of the three MUs. CV can therefore be used for MU classification.

The relationship between surface myoelectric signal parameters and the level of voluntary or electrically elicited contractions was studied in 32 experiments on the tibialis anterior muscle of 22 healthy human subjects. Contractions were performed at 20 and 80% of the maximum voluntary contraction torque. Two levels of stimulation current were used, yielding, respectively, a maximum M wave and an M wave approximately 30% of the maximum. A four-bar electrode probe was used to detect single- and double-differential signals from which mean and median frequency of the power spectrum and average muscle fiber conduction velocity were estimated. Measurements obtained from voluntary contractions showed a positive correlation between contraction levels and both conduction velocity and spectral parameters. Conduction velocity increased by 21.2 +/- 10.9% when voluntary contraction level increased from 20 to 80% of the maximal value. Spectral parameters increased by similar amounts. Tetanic electrical stimulation was applied to a muscle motor point for 20 s via surface electrodes. Rectangular current pulses with 0.1-ms width and frequencies of 20, 25, 30, 35, and 40 Hz were used. Four types of behavior were observed with increasing stimulation level: 1) the two spectral parameters and conduction velocity both increased with stimulation in 15 experiments, 2) the two spectral parameters decreased and conduction velocity increased in 8 experiments, 3) the two spectral parameters and conduction velocity both decreased in 6 experiments, and 4) the two spectral parameters increased and conduction velocity decreased in 3 experiments. Conduction velocity increased with increasing stimulation current in 72% of the experiments, indicating a recruitment order similar to that of voluntary contractions, although it decreased in the other 28% of the cases, indicating a reverse order of recruitment. Contrary to what is observed in direct stimulation of nerves, motor units are not in general recruited in reverse order of size during electrical stimulation of a muscle motor point. This discrepancy may be the result of geometric factors or a lack of correlation between axonal branch diameter and the diameter of the parent motoneuron axon. Changes of conduction velocity and spectral parameters in opposite directions may be the result of the combined effect of the motor unit recruitment order and of the different tissue filtering function associated with the geometric location of the recruited motor units within the muscle.

Summary: Objectives: To examine if different frequencies of electrical stimulation trigger different sized cramps in the abductor hallucis muscle and to analyze their surface electromyographic (EMG) behaviour in both time and frequency domains. Methods: Fifteen subjects were studied. Stimulation trains of 150 pulses were applied to the muscle motor point. Frequency was increased (starting from 4pps with 2-pps steps) until a cramp developed. Current intensity was 30% higher than that eliciting maximal M-waves. After the first cramp (“threshold cramp”), a 30-minute rest was provided before a second cramp (“above-threshold cramp”) was elicited with a frequency increased by 50% with respect to that eliciting the first cramp. Results: We found greater EMG amplitude and a compression of the power spectrum for above-threshold cramps with respect to threshold cramps. M-wave changes (ranging between small decreases of M-wave amplitude to complete M-wave disappearance) occurred and progressively increased throughout stimulation trains. Significant positive correlations were found between estimates of EMG amplitude during cramps and estimated reductions of M-wave amplitude. Conclusions: Varying frequencies of electrical stimulation triggered different sized cramps. Moreover, decreases in M-wave amplitude were observed during both threshold and above-threshold stimulations. The choice of the stimulation frequency has relevance for optimizing electrical stimulation protocols for the study of muscle cramps in both healthy and pathological subjects. [Copyright &y& Elsevier]