Fatigue compensation during FES using surface EMG

Muscle fatigue limits the effectiveness of FES when applied to regain functional movements in spinal cord injured (SCI) individuals. The stimulation intensity must be manually increased to provide more force output to compensate for the decreasing muscle force due to fatigue. An artificial neural network (ANN) system was designed to compensate for muscle fatigue during functional electrical stimulation (FES) by maintaining a constant joint angle. Surface electromyography signals (EMG) from electrically stimulated muscles were used to determine when to increase the stimulation intensity when the muscle’s output started to drop.

In two separate experiments on able-bodied subjects seated in hard back chairs, electrical stimulation was continuously applied to fatigue either the biceps (during elbow flexion) or the quadriceps muscle (during leg extension) while recording the surface EMG. An ANN system was created using processed surface EMG as the input, and a discrete fatigue compensation control signal, indicating when to increase the stimulation current, as the output. In order to provide training examples and test the systems’ performance, the stimulation current amplitude was manually increased to maintain constant joint angles. Manual stimulation amplitude increases were required upon observing a significant decrease in the joint angle. The goal of the ANN system was to generate fatigue compensation control signals in an attempt to maintain a constant joint angle.

On average, the systems could correctly predict 78.5% of the instances at which a stimulation increase was required to maintain the joint angle. The performance of these ANN systems demonstrates the feasibility of using surface EMG feedback in an FES control system.     

Consensus for experimental design in electromyography (CEDE) project: Single motor unit matrix

The analysis of single motor unit (SMU) activity provides the foundation from which information about the neural strategies underlying the control of muscle force can be identified, due to the one-to-one association between the action potentials generated by an alpha motor neuron and those received by the innervated muscle fibers. Such a powerful assessment has been conventionally performed with invasive electrodes (i.e., intramuscular electromyography (EMG)), however, recent advances in signal processing techniques have enabled the identification of single motor unit (SMU) activity in high-density surface electromyography (HDsEMG) recordings. This matrix, developed by the Consensus for Experimental Design in Electromyography (CEDE) project, provides recommendations for the recording and analysis of SMU activity with both invasive (needle and fine-wire EMG) and non-invasive (HDsEMG) SMU identification methods, summarizing their advantages and disadvantages when used during different testing conditions. Recommendations for the analysis and reporting of discharge rate and peripheral (i.e., muscle fiber conduction velocity) SMU properties are also provided. The results of the Delphi process to reach consensus are contained in an appendix. This matrix is intended to help researchers to collect, report, and interpret SMU data in the context of both research and clinical applications.     

Can forearm muscle activity be selectively recorded using conventional surface EMG-electrodes in transcranial magnetic stimulation? A feasibility study

ObjectivesThis feasibility study evaluates the effect of varying the position of conventional surface EMG-electrodes on the forearm when using Transcranial Magnetic Stimulation (TMS). The aim was to find optimal bipolar electrode positions for forearm extensor muscles, which would be clinically relevant to predict motor recovery after stroke.MethodsIn a healthy female subject, three rings of surface EMG-electrodes were placed around the dominant forearm, leading to 200 different electrode pairs. Both peripheral electrical stimulation and TMS were applied at suprathreshold intensities.ResultsWith electrical stimulation of the median and radial nerve, similar waveform morphology was found for all electrode pairs, covering both flexors and extensors. Also with TMS, remarkable similarities between all electrode pairs were found, suggesting minimal selectivity. In both peripheral electrical stimulation and TMS, the curves became more irregular with decreasing inter-electrode distances.ConclusionNeither with peripheral electrical stimulation nor with TMS it was possible to selectively record extensor or flexor forearm muscle activity using conventional surface EMG-electrodes.SignificanceDespite this negative result, the important role of the forearm extensor muscles in the prognosis of motor recovery after stroke warrants further research into novel methods for selectively recording muscle activity in TMS other than by conventional surface EMG.     

Comparison of force and EMG measures in normal and reinnervated tibialis anterior muscles of the rat.

The relationship between motor unit force and the recorded voltage produced by activated muscle unit fibres (electromyogram, EMG) was examined in normal and reinnervated rat tibialis anterior muscles. The number, cross-sectional area, and radial distance from the recording electrode of muscle fibres in a given unit, obtained directly from a sample of glycogen-depleted motor units, were analysed in relation to the magnitude of the EMG signal produced by that unit. EMG peak to peak amplitude and area varied as approximately the square root of twitch force in both normal and reinnervated units. Furthermore, the EMG amplitude increased approximately as the total cross-sectional area of the motor unit (number of muscle fibres x the average cross-sectional area of the fibres) and inversely with approximately the square root of the distance of fibres from the recording electrodes on the surface of the muscle.     

Neural muscle activation detection: A deep learning approach using surface electromyography

The timing of muscles activation which is a key parameter in determining plenty of medical conditions can be greatly assessed by the surface EMG signal which inherently carries an immense amount of information. Many techniques for measuring muscle activity detection exist in the literature. However, due to the complex nature of the EMG signal as well as the interference from other muscles that is observed during the measurement of the EMG signal, the accuracy of these techniques is compromised. In this paper, we introduce the neural muscle activation detection (NMAD) framework that detects the muscle activation based on deep learning. The main motivation behind using deep learning is to allow the neural network to detect based on the appropriate signal features instead of depending on certain assumptions. Not only the presented approach significantly improves the accuracy of timing detection, but because of the training nature, it can adapt to operate under different levels of interference and signal-to-noise ratio.     

Immediate effects of co-contraction training on motor control of the trunk muscles in people with recurrent low back pain

Although deficits in the activation of abdominal muscles are present in people with low back pain (LBP), this can be modified with motor training. Training of deep abdominal muscles in isolation from the other trunk muscles, as an initial phase of training, has been shown to improve the timing of activation of the trained muscles, and reduce symptoms and recurrence of LBP. The aim of this study was to determine if training of the trunk muscles in a non-isolated manner can restore motor control of these muscles in people with LBP. Ten subjects with non-specific LBP performed a single session of training that involved three tasks: “abdominal curl up”, “side bridge” and “birdog”. Electromyographic activity (EMG) of trunk and deltoid muscles was recorded with fine-wire and surface electrodes during rapid arm movements and walking, before and immediately following the intervention. Onset of trunk muscle EMG relative to that of the prime mover (deltoid) during arm movements and the mean, standard deviation (SD) and coefficient of variation of abdominal muscle EMG during walking were calculated. There was no significant change in the times of onset of trunk muscle EMG during arm movements nor was there any change in the variability of EMG of the abdominal muscles during walking. However, the mean amplitude and SD of abdominal EMG was reduced during walking after training. The results of this study suggest that unlike isolated voluntary training, co-contraction training of the trunk muscles does not restore the motor control of the deep abdominal muscles in people with LBP after a single session of training.     

A systematic method to quantify the presence of cross-talk in stimulus-evoked EMG responses: implications for TMS studies

Surface electromyography (EMG) responses to noninvasive nerve and brain stimulation are routinely used to provide insight into neural function in humans. However, this could lead to erroneous conclusions if evoked EMG responses contain significant contributions from neighboring muscles (i.e., due to "cross-talk"). We addressed this issue with a simple nerve stimulation method to provide quantitative information regarding the size of EMG cross-talk between muscles of the forearm and hand. Peak to peak amplitude of EMG responses to electrical stimulation of the radial, median, and ulnar nerves (i.e., M-waves) were plotted against stimulation intensity for four wrist muscles and two hand muscles (n = 12). Since electrical stimulation can selectively activate specific groups of muscles, the method can differentiate between evoked EMG arising from target muscles and EMG cross-talk arising from nontarget muscles. Intramuscular EMG responses to nerve stimulation and root mean square EMG produced during maximal voluntary contractions (MVC) of the wrist were recorded for comparison. Cross-talk was present in evoked surface EMG responses recorded from all nontarget wrist (5.05-39.38% Mmax) and hand muscles (1.50-24.25% Mmax) and to a lesser degree in intramuscular EMG signals (～3.7% Mmax). The degree of cross-talk was comparable for stimulus-evoked responses and voluntary activity recorded during MVC. Since cross-talk can make a considerable contribution to EMG responses in forearm and hand muscles, care is required to avoid misinterpretation of EMG data. The multiple nerve stimulation method described here can be used to quantify the potential contribution of EMG cross-talk in transcranial magnetic stimulation and reflex studies.     

The extraction of neural strategies from the surface EMG.

This brief review examines some of the methods used to infer central control strategies from surface electromyogram (EMG) recordings. Among the many uses of the surface EMG in studying the neural control of movement, the review critically evaluates only some of the applications. The focus is on the relations between global features of the surface EMG and the underlying physiological processes. Because direct measurements of motor unit activation are not available and many factors can influence the signal, these relations are frequently misinterpreted. These errors are compounded by the counterintuitive effects that some system parameters can have on the EMG signal. The phenomenon of crosstalk is used as an example of these problems. The review describes the limitations of techniques used to infer the level of muscle activation, the type of motor unit recruited, the upper limit of motor unit recruitment, the average discharge rate, and the degree of synchronization between motor units. Although the global surface EMG is a useful measure of muscle activation and assessment, there are limits to the information that can be extracted from this signal.     

Rectus femoris surface myoelectric signal cross-talk during static contractions

The clinical application of EMG requires that the recorded signal is representative of the muscle of interest and is not contaminated with signals from adjacent muscles. Some authors report that surface EMG is not suitable for obtaining information on a single muscle but rather reflects muscle group function [J. Perry, C.S. Easterday, D.J. Antonelli, Surface versus intramuscular electrodes for electromyography of superficial and deep muscles. Physical Therapy 61 (1981) 7–15]. Other authors report however, that surface EMG is adequate to determine individual muscle function, once guidelines pertaining to data acquisition are followed [D.A. Winter, A.J. Fuglevand, S.E. Archer. Cross-talk in surface electromyography: theoretical and practical estimates. Journal of Electromyography and Kinesiology 4 (1994) 15–26]. The aim of this study was to determine whether surface EMG was suitable for monitoring rectus femoris (RF) activity during static contractions. Five healthy subjects, having given written informed consent, participated in this trial. Surface and fine wire EMG from the rectus femoris and the vastus lateralis (VL) muscles were recorded simultaneously during a protocol of static contractions consisting of knee extensions and hip flexions. Ratios were used to quantify the relationship between the surface EMG amplitude value and the fine wire EMG amplitude value for the same contraction. The results showed that hip flexion contractions elicited RF activation only and that knee extension contractions elicited fine wire activity in VL only. When the relationship between RF surface and RF fine wire electrodes was compared for hip flexion and knee extension contractions, it was observed that for all subjects, there was a tendency for increased RF surface activity in the absence of RF fine wire activity during knee extensions. It was concluded that the activity recorded by the RF surface electrode arrangement during knee extension consisted of EMG from the vastii, i.e., cross-talk and that vastus intermedius was the most likely origin of the erroneous signal. Therefore it is concluded that for accurate EMG information from RF, fine wire electrodes are necessary during a range of static contractions.     

The neural control of coactivation during fatiguing contractions revisited

In addition to the role of muscle coactivation, a major question in the field is how antagonist activation is controlled to minimize its opposing effect on agonist muscle performance. Muscle fatigue is an interesting condition to analyze the neural adjustments in antagonist muscle activity and to gain more insights into the control mechanisms of coactivation. In that context, previous studies have reported that although the EMG activity of agonists and antagonists increase in parallel, the ratio between EMG activities in the two sets of muscles during a fatiguing submaximal contraction decreased progressively and contributed to a reduction in the time to task failure. In contrast, more recent studies using a novel normalization procedure indicated that the agonist/antagonist ratio remained relatively constant, suggesting that the fatigue-related increase in coactivation does not impede performance. Current knowledge also indicates that peripheral mechanisms cannot by themselves mediate the intensity of antagonist coactivation during fatiguing contractions, implying that supraspinal mechanisms are involved. The unique modulation of the synaptic input from Ia afferents to the antagonist motor neurones during a fatiguing contraction of the agonist muscles further suggests a separate control of the two sets of muscles.     

Electromyogram recordings from freely moving animals

Electromyography can be used to record activity from sets of muscles in awake, freely moving animals using implanted intramuscular electrodes. As a tool, EMG has a wide range of applications ranging from inferring neural processes to analyzing movement. The amplitude of the rectified and filtered electromyogram (EMG) can be used as an indirect measure of muscle activity. Although it is often tempting to correlate the EMG with muscle force, the fact that force varies more with different activation strategies than with EMG estimates must be taken into account. The purpose of this article is to provide the researcher wishing to introduce the technique of recording EMGs from conscious animals using intramuscular electrodes with a step-by-step guide. It includes details on the manufacture of electromyograph electrodes, recording, and analysis considerations along with a section on solving common problems. For the sake of clarity, this article focuses on using the cat as a model and on the implantation of hindlimb muscles with intramuscular wire electrodes. However, the procedures can be adapted for use on other striated muscles and species.     

Recent progress in the diagnostic use of surface EMG for neurological diseases.

The different techniques to measure and analyze surface EMG are summarized with an emphasis on the clinician's point of view. The application of surface EMG in neurological disease is hampered by many inherent problems, especially the difficulties in extracting features of single motor units. However, the evolution of surface EMG from single bipolar recordings via a linear array of multiple electrodes to densely packed, multi-channel electrode arrays could in principle solve this problem. The added value of using multiple channels (up to 128) with an interelectrode distance of a few millimetres to obtain more spatial information is emphasized. At least for some muscles it is now possible to extract information from the surface EMG, conventionally thought to belong to the domain of needle EMG (for example the "electrical size" of motor units). The use of analysis techniques such as the estimation of muscle fiber conduction velocity has already proven to be of diagnostic value in several myopathies characterized by a disturbed membrane function and in metabolic myopathies with abnormal fatigue profiles. Future research should be directed at the development of analysis techniques enabling the extraction of more relevant motor unit variables from surface EMG signals.     

Real-time intelligent pattern recognition algorithm for surface EMG signals

Background  

Electromyography (EMG) is the study of muscle function through the inquiry of electrical signals that the muscles emanate. EMG signals collected from the surface of the skin (Surface Electromyogram: sEMG) can be used in different applications such as recognizing musculoskeletal neural based patterns intercepted for hand prosthesis movements. Current systems designed for controlling the prosthetic hands either have limited functions or can only be used to perform simple movements or use excessive amount of electrodes in order to achieve acceptable results. In an attempt to overcome these problems we have proposed an intelligent system to recognize hand movements and have provided a user assessment routine to evaluate the correctness of executed movements.     

Electrophysiological Motor Unit Number Estimation (MUNE) Measuring Compound Muscle Action Potential (CMAP) in Mouse Hindlimb Muscles

Compound muscle action potential (CMAP) and motor unit number estimation (MUNE) are electrophysiological techniques that can be used to monitor the functional status of a motor unit pool in vivo. These measures can provide insight into the normal development and degeneration of the neuromuscular system. These measures have clear translational potential because they are routinely applied in diagnostic and clinical human studies. We present electrophysiological techniques similar to those employed in humans to allow recordings of mouse sciatic nerve function. The CMAP response represents the electrophysiological output from a muscle or group of muscles following supramaximal stimulation of a peripheral nerve. MUNE is an electrophysiological technique that is based on modifications of the CMAP response. MUNE is a calculated value that represents the estimated number of motor neurons or axons (motor control input) supplying the muscle or group of muscles being tested. We present methods for recording CMAP responses from the proximal leg muscles using surface recording electrodes following the stimulation of the sciatic nerve in mice. An incremental MUNE technique is described using submaximal stimuli to determine the average single motor unit potential (SMUP) size. MUNE is calculated by dividing the CMAP amplitude (peak-to-peak) by the SMUP amplitude (peak-to-peak). These electrophysiological techniques allow repeated measures in both neonatal and adult mice in such a manner that facilitates rapid analysis and data collection while reducing the number of animals required for experimental testing. Furthermore, these measures are similar to those recorded in human studies allowing more direct comparisons.     

Brainstem Circuitry Regulating Phasic Activation of Trigeminal Motoneurons during REM Sleep

Background

Rapid eye movement sleep (REMS) is characterized by activation of the cortical and hippocampal electroencephalogram (EEG) and atonia of non-respiratory muscles with superimposed phasic activity or twitching, particularly of cranial muscles such as those of the eye, tongue, face and jaw. While phasic activity is a characteristic feature of REMS, the neural substrates driving this activity remain unresolved. Here we investigated the neural circuits underlying masseter (jaw) phasic activity during REMS. The trigeminal motor nucleus (Mo5), which controls masseter motor function, receives glutamatergic inputs mainly from the parvocellular reticular formation (PCRt), but also from the adjacent paramedian reticular area (PMnR). On the other hand, the Mo5 and PCRt do not receive direct input from the sublaterodorsal (SLD) nucleus, a brainstem region critical for REMS atonia of postural muscles. We hypothesized that the PCRt-PMnR, but not the SLD, regulates masseter phasic activity during REMS.

Methodology/Principal Findings

To test our hypothesis, we measured masseter electromyogram (EMG), neck muscle EMG, electrooculogram (EOG) and EEG in rats with cell-body specific lesions of the SLD, PMnR, and PCRt. Bilateral lesions of the PMnR and rostral PCRt (rPCRt), but not the caudal PCRt or SLD, reduced and eliminated REMS phasic activity of the masseter, respectively. Lesions of the PMnR and rPCRt did not, however, alter the neck EMG or EOG. To determine if rPCRt neurons use glutamate to control masseter phasic movements, we selectively blocked glutamate release by rPCRt neurons using a Cre-lox mouse system. Genetic disruption of glutamate neurotransmission by rPCRt neurons blocked masseter phasic activity during REMS.

Conclusions/Significance

These results indicate that (1) premotor glutamatergic neurons in the medullary rPCRt and PMnR are involved in generating phasic activity in the masseter muscles, but not phasic eye movements, during REMS; and (2) separate brainstem neural circuits control postural and cranial muscle phasic activity during REMS.     

Further evaluation of an EMG technique for assessment of the deep cervical flexor muscles

A novel surface electromyographic (EMG) technique was recently described for the detection of deep cervical flexor muscle activity. Further investigation of this technique is warranted to ensure EMG activity from neighbouring muscles is not markedly influencing the signals recorded. This study compared deep cervical flexor (DCF) muscle activity with the activity of surrounding neck and jaw muscles during various anatomical movements of the neck and jaw in 10 volunteer subjects. DCF EMG activity was recorded with custom electrodes inserted via the nose and fixed by suction to the posterior mucosa of the oropharynx. Surface electrodes were placed over the sternocleidomastoid, anterior scalene, masseter and suprahyoid muscles. Positioned in supine, subjects performed isometric cranio-cervical flexion, cervical flexion, right and left cervical rotation, jaw clench and resisted jaw opening. Across all movements examined, EMG amplitude of the DCF muscles was greatest during neck movements that would require activity of the DCF muscles, particularly during cranio-cervical flexion, their primary anatomical action. The actions of jaw clench and resisted jaw opening demonstrated significantly less DCF EMG activity than the cranio-cervical flexion action (p < 0.05). Across all other movements, the neighbouring neck and jaw muscles demonstrated greatest EMG amplitude during their respective primary anatomical actions, which occurred in the absence of increased EMG amplitude recorded from the DCF muscles. The finding of substantial EMG activity of the DCF muscles only during neck actions that would require their activity, particularly cranio-cervical flexion, and not during actions involving the jaw, provide further assurance that the majority of myoelectric signals detected from the nasopharyngeal electrode are from the DCF muscles.     

Methodological aspects of SEMG recordings for force estimation – A tutorial and review

Insight into the magnitude of muscle forces is important in biomechanics research, for example because muscle forces are the main determinants of joint loading. Unfortunately muscle forces cannot be calculated directly and can only be measured using invasive procedures. Therefore, estimates of muscle force based on surface EMG measurements are frequently used. This review discusses the problems associated with surface EMG in muscle force estimation and the solutions that novel methodological developments provide to this problem. First, some basic aspects of muscle activity and EMG are reviewed and related to EMG amplitude estimation. The main methodological issues in EMG amplitude estimation are precision and representativeness. Lack of precision arises directly from the stochastic nature of the EMG signal as the summation of a series of randomly occurring polyphasic motor unit potentials and the resulting random constructive and destructive (phase cancellation) superimpositions. Representativeness is an issue due the structural and functional heterogeneity of muscles. Novel methods, i.e. multi-channel monopolar EMG and high-pass filtering or whitening of conventional bipolar EMG allow substantially less variable estimates of the EMG amplitude and yield better estimates of muscle force by (1) reducing effects of phase cancellation, and (2) adequate representation of the heterogeneous activity of motor units within a muscle. With such methods, highly accurate predictions of force, even of the minute force fluctuations that occur during an isometric and isotonic contraction have been achieved. For dynamic contractions, EMG-based force estimates are confounded by the effects of muscle length and contraction velocity on force producing capacity. These contractions require EMG amplitude estimates to be combined with modeling of muscle contraction dynamics to achieve valid force predictions.     

Modular neuromuscular control of human locomotion by central pattern generator

The central pattern generators (CPG) in the spinal cord are thought to be responsible for producing the rhythmic motor patterns during rhythmic activities. For locomotor tasks, this involves much complexity, due to a redundant system of muscle actuators with a large number of highly nonlinear muscles. This study proposes a reduced neural control strategy for the CPG, based on modular organization of the co-active muscles, i.e., muscle synergies. Four synergies were extracted from the EMG data of the major leg muscles of two subjects, during two gait trials each, using non-negative matrix factorization algorithm. A Matsuoka׳s four-neuron CPG model with mutual inhibition, was utilized to generate the rhythmic activation patterns of the muscle synergies, using the hip flexion angle and foot contact force information from the sensory afferents as inputs. The model parameters were tuned using the experimental data of one gait trial, which resulted in a good fitting accuracy (RMSEs between 0.0491 and 0.1399) between the simulation and experimental synergy activations. The model׳s performance was then assessed by comparing its predictions for the activation patterns of the individual leg muscles during locomotion with the relevant EMG data. Results indicated that the characteristic features of the complex activation patterns of the muscles were well reproduced by the model for different gait trials and subjects. In general, the CPG- and muscle synergy-based model was promising in view of its simple architecture, yet extensive potentials for neuromuscular control, e.g., resolving redundancies, distributed and fast control, and modulation of locomotion by simple control signals.     

Influence of amplitude cancellation on the accuracy of determining the onset of muscle activity from the surface electromyogram

The purpose of the study was to quantify the influence of amplitude cancellation on the accuracy of detecting the onset of muscle activity based on an analysis of simulated surface electromyographic (EMG) signals. EMG activity of a generic lower limb muscle was simulated during the stance phase of human gait. Surface EMG signals were generated with and without amplitude cancellation by summing simulated motor unit potentials either before (cancellation EMG) or after (no-cancellation EMG) the potentials had been rectified. The two sets of EMG signals were compared at forces of 30% and 80% of maximum voluntary contraction (MVC) and with various low-pass filter cut-off frequencies. Onset time was determined both visually and by an algorithm that identified when the mean amplitude of the signal within a sliding window exceeded a specified standard deviation (SD) above the baseline mean. Onset error was greater for the no-cancellation conditions when determined automatically and by visual inspection. However, the differences in onset error between the two cancellation conditions appear to be clinically insignificant. Therefore, amplitude cancellation does not appear to limit the ability to detect the onset of muscle activity from the surface EMG.     

Neuromuscular adjustments that constrain submaximal EMG amplitude at task failure of sustained isometric contractions

The amplitude of the surface EMG does not reach the level achieved during a maximal voluntary contraction force at the end of a sustained, submaximal contraction, despite near-maximal levels of voluntary effort. The depression of EMG amplitude may be explained by several neural and muscular adjustments during fatiguing contractions, including decreased net neural drive to the muscle, changes in the shape of the motor unit action potentials, and EMG amplitude cancellation. The changes in these parameters for the entire motor unit pool, however, cannot be measured experimentally. The present study used a computational model to simulate the adjustments during sustained isometric contractions and thereby determine the relative importance of these factors in explaining the submaximal levels of EMG amplitude at task failure. The simulation results indicated that the amount of amplitude cancellation in the simulated EMG (～ 40%) exhibited a negligible change during the fatiguing contractions. Instead, the main determinant of the submaximal EMG amplitude at task failure was a decrease in muscle activation (number of muscle fiber action potentials), due to a reduction in the net synaptic input to motor neurons, with a lesser contribution from changes in the shape of the motor unit action potentials. Despite the association between the submaximal EMG amplitude and reduced muscle activation, the deficit in EMG amplitude at task failure was not consistently associated with the decrease in neural drive (number of motor unit action potentials) to the muscle. This indicates that the EMG amplitude cannot be used as an index of neural drive.