A model of EMG generation

Characteristics of corticomuscular coupling during wheelchair Tai Chi in patients with spinal cord injury

BACKGROUND

Wheelchair Tai Chi (WCTC) has been proved to have benefits for the brain and motor system of spinal cord injury (SCI) patients. However, the characteristics of corticomuscular coupling during WCTC are scarcely known. We aimed to investigate changes following SCI on corticomuscular coupling, and further compare the coupling characteristics of WCTC with aerobic exercise in SCI patients.

METHODS

A total of 15 SCI patients and 25 healthy controls were recruited. The patients had to perform aerobic exercise and WCTC, while healthy controls needed to complete a set of WCTC. The participants accomplished the test following the tutorial video in a sitting position. The upper limb muscle activation was measured from upper trapezius, medial deltoid, biceps brachii and triceps brachii with surface electromyography. Cortical activity in the prefrontal cortex, premotor cortex, supplementary motor area and primary motor cortex was simultaneously collected by functional near-infrared spectroscopy. The functional connectivity, phase synchronization index and coherence values were then calculated and statistically analyzed.

RESULTS

Compared to healthy controls, changes in functional connectivity and higher muscle activation were observed in the SCI group. There was no significant difference in phase synchronization between groups. Among patients, significantly higher coherence values between the left biceps brachii as well as the right triceps brachii and contralateral regions of interest were found during WCTC than during aerobic exercise.

CONCLUSION

The patients may compensate for the lack of corticomuscular coupling by enhancing muscle activation. This study demonstrated the potential and advantages of WCTC in eliciting corticomuscular coupling, which may optimize rehabilitation following SCI.

Nonhomogeneous volume conduction effects affecting needle electromyography: an analytical and simulation study

Objective.Needle electromyography (EMG) is used to study the electrical behavior of myofiber properties in patients with neuromuscular disorders. However, due to the complexity of electrical potential spatial propagation in nonhomogeneous diseased muscle, a comprehensive understanding of volume conduction effects remains elusive. Here, we develop a framework to study the conduction effect of extracellular abnormalities and electrode positioning on extracellular local field potential (LFP) recordings.Methods.The framework describes the macroscopic conduction of electrical potential in an isotropic, nonhomogeneous (i.e. two tissue) model. Numerical and finite element model simulations are provided to study the conduction effect in prototypical monopolar EMG measurements.Results.LFPs recorded are influenced in amplitude, phase and duration by the electrode position in regards to the vicinity of tissue with different electrical properties.Conclusion.The framework reveals the influence of multiple mechanisms affecting LFPs including changes in the distance between the source-electrode and tissue electrical properties.Clinical significance.Our modeled predictions may lead to new ways for interpreting volume conduction effects on recorded EMG activity, for example in neuromuscular diseases that cause structural and compositional changes in muscle tissue. These change will manifest itself by changing the electric properties of the conductor media and will impact recorded potentials in the area of affected tissue.

Adaptive Real-time Decomposition of Electromyogram During Sustained Muscle Activation: A Simulation Study

OBJECTIVE

Real-time decomposition of electromyogram (EMG) into constituent motor unit (MU) activity has shown promising applications in neurophysiology and human-machine interactions. Existing decomposition methods could not accommodate stochastic variations in EMG signals such as drifts of action potential amplitudes and MU recruitment-derecruitment (rotation) patterns during long-term recordings. The objective of this study was to develop an adaptive real-time decomposition approach suitable for prolonged muscle activation.

METHODS

We developed a parallel-double-thread computation algorithm. The backend thread initiated and periodically refined and updated the MU information (separation matrix) using independent component analysis and convolution kernel compensation. The frontend thread performed the real-time decomposition. We evaluated our algorithm on synthesized high-density EMG signals, in which MUs were recruited-derecruited sporadically and MU action potentials amplitude drifted over time. Different signal-to-noise levels were also simulated.

RESULTS

Compared with the decomposition without the adaptive processes, periodically fine-tuned and updated separation matrix increased identifiable MU number by 3-4 fold over 30-minute of recordings. The increased MU number was more prominent at higher signal-to-noise ratios. The decomposition accuracy also increased by up to 10% with greater improvement observed at higher muscle contraction levels.

CONCLUSION

The adaptive algorithm can maintain the decomposition performance over time, allows us to continuously track the same MUs during sustained activation, and, at the same time, can add newly recruited MU information to existing separation matrix.

SIGNIFICANCE

Our approach showed robust performance over time, which has the potential to longitudinally evaluate MU firing and recruitment properties and improve neural decoding performance for neural-machine interactions.

Model-Based Analysis of Muscle Strength and EMG-Force Relation with respect to Different Patterns of Motor Unit Loss

This study presents a model-based sensitivity analysis of the strength of voluntary muscle contraction with respect to different patterns of motor unit loss. A motor unit pool model was implemented including simulation of a motor neuron pool, muscle force, and surface electromyogram (EMG) signals. Three different patterns of motor unit loss were simulated, including (1) motor unit loss restricted to the largest ones, (2) motor unit loss restricted to the smallest ones, and (3) motor unit loss without size restriction. The model outputs including muscle force amplitude, variability, and the resultant EMG-force relation were quantified under two different motor neuron firing strategies. It was found that motor unit loss restricted to the largest ones had the most dominant impact on muscle strength and significantly changed the EMG-force relation, while loss restricted to the smallest motor units had a pronounced effect on force variability. These findings provide valuable insight toward our understanding of the neurophysiological mechanisms underlying experimental observations of muscle strength, force control, and EMG-force relation in both normal and pathological conditions.

Electromyogram (EMG) Removal by Adding Sources of EMG (ERASE)-A Novel ICA-Based Algorithm for Removing Myoelectric Artifacts From EEG

Electroencephalographic (EEG) recordings are often contaminated by electromyographic (EMG) artifacts, especially when recording during movement. Existing methods to remove EMG artifacts include independent component analysis (ICA), and other high-order statistical methods. However, these methods can not effectively remove most of EMG artifacts. Here, we proposed a modified ICA model for EMG artifacts removal in the EEG, which is called EMG Removal by Adding Sources of EMG (ERASE). In this new approach, additional channels of real EMG from neck and head muscles (reference artifacts) were added as inputs to ICA in order to “force” the most power from EMG artifacts into a few independent components (ICs). The ICs containing EMG artifacts (the “artifact ICs”) were identified and rejected using an automated procedure. ERASE was validated first using both simulated and experimentally-recorded EEG and EMG. Simulation results showed ERASE removed EMG artifacts from EEG significantly more effectively than conventional ICA. Also, it had a low false positive rate and high sensitivity. Subsequently, EEG was collected from 8 healthy participants while they moved their hands to test the realistic efficacy of this approach. Results showed that ERASE successfully removed EMG artifacts (on average, about 75% of EMG artifacts were removed when using real EMGs as reference artifacts) while preserving the expected EEG features related to movement. We also tested the ERASE procedure using simulated EMGs as reference artifacts (about 63% of EMG artifacts removed). Compared to conventional ICA, ERASE removed on average 26% more EMG artifacts from EEG. These findings suggest that ERASE can achieve significant separation of EEG signal and EMG artifacts without a loss of the underlying EEG features. These results indicate that using additional real or simulated EMG sources can increase the effectiveness of ICA in removing EMG artifacts from EEG. Combined with automated artifact IC rejection, ERASE also minimizes potential user bias. Future work will focus on improving ERASE so that it can also be used in real-time applications.

Refinement of High-Gamma EEG Features From TBI Patients With Hemicraniectomy Using an ICA Informed by Simulated Myoelectric Artifacts

Recent studies have shown the ability to record high-γ signals (80-160 Hz) in electroencephalogram (EEG) from traumatic brain injury (TBI) patients who have had hemicraniectomies. However, extraction of the movement-related high-γ remains challenging due to a confounding bandwidth overlap with surface electromyogram (EMG) artifacts related to facial and head movements. In our previous work, we described an augmented independent component analysis (ICA) approach for removal of EMG artifacts from EEG, and referred to as EMG Reduction by Adding Sources of EMG (ERASE). Here, we tested this algorithm on EEG recorded from six TBI patients with hemicraniectomies while they performed a thumb flexion task. ERASE removed a mean of 52 ± 12% (mean ± S.E.M) (maximum 73%) of EMG artifacts. In contrast, conventional ICA removed a mean of 27 ± 19% (mean ± S.E.M) of EMG artifacts from EEG. In particular, high-γ synchronization was significantly improved in the contralateral hand motor cortex area within the hemicraniectomy site after ERASE was applied. A more sophisticated measure of high-γ complexity is the fractal dimension (FD). Here, we computed the FD of EEG high-γ on each channel. Relative FD of high-γ was defined as that the FD in move state was subtracted by FD in idle state. We found relative FD of high-γ over hemicraniectomy after applying ERASE were strongly correlated to the amplitude of finger flexion force. Results showed that significant correlation coefficients across the electrodes related to thumb flexion averaged ~0.76, while the coefficients across the homologous electrodes in non-hemicraniectomy areas were nearly 0. After conventional ICA, a correlation between relative FD of high-γ and force remained high in both hemicraniectomy areas (up to 0.86) and non-hemicraniectomy areas (up to 0.81). Across all subjects, an average of 83% of electrodes significantly correlated with force was located in the hemicraniectomy areas after applying ERASE. After conventional ICA, only 19% of electrodes with significant correlations were located in the hemicraniectomy. These results indicated that the new approach isolated electrophysiological features during finger motor activation while selectively removing confounding EMG artifacts. This approach removed EMG artifacts that can contaminate high-gamma activity recorded over the hemicraniectomy.

High-Density Surface EMG Denoising Using Independent Vector Analysis

High-density surface electromyography (HD-sEMG) can provide rich temporal and spatial information about muscle activation. However, HD-sEMG signals are often contaminated by power line interference (PLI) and white Gaussian noise (WGN). In the literature, independent component analysis (ICA) and canonical correlation analysis (CCA), as two popular used blind source separation techniques, are widely used for noise removal from HD-sEMG signals. In this paper, a novel method to remove PLI and WGN was proposed based on independent vector analysis (IVA). Taking advantage of both ICA and CCA, this method exploits the higher order and second-order statistical information simultaneously. Our proposed method was applied to both simulated and experimental EMG data for performance evaluation, which was at least 37.50% better than ICA and CCA methods in terms of relative root mean squared error and 28.84% better than ICA and CCA methods according to signal to noise ratio. The results demonstrated that our proposed method performed significantly better than either ICA or CCA. Specifically, the mean signal to noise ratio increased considerably. Our proposed method is a promising tool for denoising HD-sEMG signals while leading to a minimal distortion.

A novel algorithm for removing artifacts from EEG data

In recent years, many studies examined if EEG signals from traumatic brain injury (TBI) patients can be used for new rehabilitation technologies, such as BCI systems. However, extraction of the high-gamma band related to movement remains challenging due to the presence of surface electromyogram (sEMG) caused by unconscious facial and head movement of patients. In this paper, we proposed a modified independent component analysis (ICA) model for EMG artifact removal in the EEG data from TBI patients with a hemicraniectomy. Here, simulated EMG was generated and added to the raw EEG data as the extra channels for independent components calculation. After running ICA, the independent components (ICs) related to artifacts were identified and rejected automatically through several criteria. EEG data underlying hand movement from one healthy subject and one TBI patient with a hemicraniectomy were conducted to verify the efficacy of this algorithm. Results showed that the proposed algorithm removed sEMG artifacts from the EEG data by up to 86.72% while preserving the associated brain features. In particular, the high-gamma band (80 to 160 Hz) was found to arise principally from the hemicraniectomy area after this technique was applied. Meanwhile, we found that the magnitude of gamma power during movement improved after removal of sEMG artifacts.

Co-Contraction of Lower Limb Muscles Contributes to Knee Stability During Stance Phase in Hemiplegic Stroke Patients

Background

Knee stability has an important role in the gait of hemiplegic stroke patients. However, factors affecting knee stability have not been assessed concerning gait. The purpose of this study was to explore whether co-contraction of the lower limb muscles contributes to the knee stability during the stance phase of the gait cycle in hemiplegic stroke patients.

Material/Methods

A total of 30 hemiplegic stroke patients, ages 36–79 years, were instructed to walk at their natural speed. The root mean square of surface electromyography was used to measure activities of the biceps femoris and rectus femoris muscles, while the co-contraction ratio was computed based on the root mean squares. The peak angle of knee extension was acquired in the stance phase by 3D kinematic analyses. Lower limb function was evaluated using the Fugl-Meyer scale for lower limb motor assessment.

Results

A statistically significant increase of the muscle co-contraction ratio of the involved extremity was observed compared with that of the uninvolved extremity (t=−4.066, P<0.05). The muscle co-contraction ratio was significantly correlated with the peak angle of knee extension (r=0.387, P=0.035), Fugl-Meyer scale (r=−0.522, P=0.003), and Modified Ashworth Scale (r=0.404, P=0.027) during the stance phase of the gait cycle.

Conclusions

Our results showed that co-contraction of the rectus femoris muscle contributes to the stability of the knee and lower limb function in hemiplegic stroke patients, and suggests that co-contraction should be considered in the rehabilitation of knee stability during gait in hemiplegic stroke patients. Appropriate rehabilitation assessment planning with hemiplegic stroke patients, such as muscle co-contraction or knee stability of, might be created based on our results.

Methodology of surface electromyography in gait analysis: review of the literature

Gait analysis is a significant diagnostic procedure for the clinicians who manage musculoskeletal disorders. Surface electromyography (sEMG) combined with kinematic and kinetic data is a useful tool for decision making of the appropriate method needed to treat such patients. sEMG has been used for decades to evaluate neuromuscular responses during a range of activities and develop rehabilitation protocols. The sEMG methodology followed by researchers assessed the issues of noise control, wave frequency, cross talk, low signal reception, muscle co-contraction, electrode placement protocol and procedure as well as EMG signal timing, intensity and normalisation so as to collect accurate, adequate and meaningful data. Further research should be done to provide more information related to the muscle activity recorded by sEMG and the force produced by the corresponding muscle during gait analysis.

A Novel Framework Based on FastICA for High Density Surface EMG Decomposition

This study presents a progressive FastICA peel-off (PFP) framework for high-density surface electromyogram (EMG) decomposition. The novel framework is based on a shift-invariant model for describing surface EMG. The decomposition process can be viewed as progressively expanding the set of motor unit spike trains, which is primarily based on FastICA. To overcome the local convergence of FastICA, a peel-off strategy, i.e., removal of the estimated motor unit action potential trains from the previous step, is used to mitigate the effects of the already identified motor units, so more motor units can be extracted. A constrained FastICA is applied to assess the extracted spike trains and correct possible erroneous or missed spikes. These procedures work together to improve decomposition performance. The proposed framework was validated using simulated surface EMG signals with different motor unit numbers (30, 70, 91) and SNRs (20, 10, and 0 dB). The results demonstrated relatively large numbers of extracted motor units and high accuracies (high F1-scores). The framework was tested with 111 trials of 64-channel electrode array experimental surface EMG signals during the first dorsal interosseous muscle contraction at different intensities. On average 14.1 ±5.0 motor units were identified from each trial of experimental surface EMG signals.

Verification of the muscle fatigue detection capability of a unipolar-leads system using a surface electromyogram model

In this study, the muscle fatigue detection capability of bipolar and unipolar lead systems used for surface electromyogram measurement was verified by simulation. The constructed model simplified the isometric contraction of the biceps brachii. There were two simulation experiments: 1) the addition and deletion of white noise and 2) the addition and deletion of hum noise. The pattern result of simulation 1) suggested the possibility that the muscle fatigue detection capability of a unipolar-leads system was high. The pattern 2) result showed the unipolar-leads system had a small influence of filtering, and suggested that the mixing of hum noise could be disregarded.

Complexity analysis of EMG signals for patients after stroke during robot-aided rehabilitation training using fuzzy approximate entropy

The paper presents a novel viewpoint to monitor the motor function improvement during a robot-aided rehabilitation training. Eight chronic poststroke subjects were recruited to attend the 20-session training, and in each session, subjects were asked to perform voluntary movements of elbow flexion and extension together with the robotic system. The robotic system was continuously controlled by the electromyographic (EMG) signal from the affected triceps. Fuzzy approximate entropy (fApEn) was applied to investigate the complexity of the EMG segment, and maximum voluntary contraction (MVC) during elbow flexion and extension was applied to reflect force generating capacity of the affected muscles. The results showed that the group mean fApEn of EMG signals from triceps and biceps increased significantly after the robot-aided rehabilitation training . There was also significant increase in maximum voluntary flexion and extension torques after the robot-aided rehabilitation training . There was significant correlation between fApEn of agonist and MVC , which implied that the increase of motorneuron number is one of factors that may explain the increase in muscle strength. These findings based on fApEn of the EMG signals expand the existing interpretation of training-induced function improvement in patients after stroke, and help us to understand the neurological change induced by the robot-aided rehabilitation training.

A novel spatiotemporal muscle activity imaging approach based on the Extended Kalman Filter

A novel spatiotemporal muscle activity imaging (sMAI) approach has been developed using the Extended Kalman Filter (EKF) to reconstruct internal muscle activities from non-invasive multi-channel surface electromyogram (sEMG) recordings. A distributed bioelectric dipole source model is employed to describe the internal muscle activity space, and a linear relationship between the muscle activity space and the sEMG measurement space is then established. The EKF is employed to recursively solve the ill-posed inverse problem in the sMAI approach, in which the weighted minimum norm (WMN) method is utilized to calculate the initial state and a new nonlinear method is developed based on the propagating features of muscle activities to predict the recursive state. A series of computer simulations was conducted to test the performance of the proposed sMAI approach. Results show that the localization error rapidly decreases over 35% and the overlap ratio rapidly increases over 45% compared to the results achieved using the WMN method only. The present promising results demonstrate the feasibility of utilizing the proposed EKF-based sMAI approach to accurately reconstruct internal muscle activities from non-invasive sEMG recordings.

Volume conductor models in surface electromyography: computational techniques

Models of surface electromyogram (EMG) are useful to assess the effect of geometrical or conductivity properties of the tissue on the recorded signal. This paper provides a review of structure based models describing specific volume conductors. The technique for the development of advanced analytical and numerical simulators is described. A new model is also introduced, simulating a layered volume conductor including a subcutaneous tissue with variable thicknesses, providing an approximate analytical solution in the Fourier transform domain. Note that volume conductors are described using Poisson equation, fundamental model of Mathematical Physics, which applies also to mechanics, diffusion, electrostatics problems.

High-density surface EMG decomposition based on a convolutive blind source separation approach

A novel automatic approach is developed in the present study to decompose high density surface electromyography (EMG) signals into motor unit (MU) firing patterns. The observed surface EMG signals are first modeled as a convolutive mixture of active MU sources. Contrast function maximization is employed to extract the first source, and separation of other sources is then carried out by an iterative deflation approach. Each extracted source is further processed and verified with the characteristics of motor unit action potential and firing patterns. The performance of the proposed automatic approach is evaluated in well-designed computer simulation. Results show that 4.7±0.5 and 7.1±0.6 MUs were correctly identified in the case of 5 and 10 active MUs respectively.

An Empirical Muscle Intracellular Action Potential Model with Multiple Erlang Probability Density Functions based on a Modified Newton Method

The convolution of the transmembrane current of an excitable cell and a weighting function generates a single fiber action potential (SFAP) model by using the volume conductor theory. Here, we propose an empirical muscle IAP model with multiple Erlang probability density functions (PDFs) based on a modified Newton method. In addition, we generate SFAPs based on our IAP model and referent sources, and use the peak-to-peak ratios (PPRs) of SFAPs for model verification. Through this verification, we find that the relation between an IAP profile and the PPR of its SFAP is consistent with some previous studies, and our IAP model shows close profiles to the referent sources. Moreover, we simulate and discuss some possible ionic activities by using the Erlang PDFs in our IAP model, which might present the underlying activities of ions or their channels during an IAP.

A simulation study on the relation between muscle motor unit numbers and the non-Gaussianity/non-linearity levels of surface electromyography

Recent research has demonstrated that surface electromyography (sEMG) signals have non-Gaussianity and non-linearity properties. It is known that more muscle motor units are recruited and firing rates (FRs) increase as exertion increases. A hypothesis was proposed that the Gaussianity test (S (g)) and linearity test (S (ℓ)) levels of sEMG signals are associated with the number of active motor units (nMUs) and the FR. The hypothesis has only been preliminarily discussed in experimental studies. We used a simulation sEMG model involving spatial (active MUs) and temporal (three FRs) information to test the hypothesis. Higher-order statistics (HOS) from the bi-frequency domain were used to perform S (g) and S (ℓ). Multivariate covariance analysis and a correlation test were employed to determine the nMUs-S (g) relationship and the nMUs-S (ℓ) relationship. Results showed that nMUs, the FR, and the interaction of nMUs and the FR all influenced the S (g) and S (ℓ) values. The nMUs negatively correlated to both the S (g) and S (ℓ) values. That is, at the three FRs, sEMG signals tended to a more Gaussian and linear distribution as exertion and nMUs increased. The study limited experiment factors to the sEMG non-Gaussianity and non-linearity levels. The study quantitatively described nMUs and the FR of muscle that are not directly available from experiments. Our finding has guiding significance for muscle capability assessment and prosthetic control.

Insights gained into the interpretation of surface electromyograms from the gastrocnemius muscles: A simulation study

Interpretation of surface electromyograms (EMG) is usually based on the assumption that the surface representation of action potentials does not change during their propagation. This assumption does not hold for muscles whose fibers are oblique to the skin. Consequently, the interpretation of surface EMGs recorded from pinnate muscles unlikely prompts from current knowledge. Here we present a complete analytical model that supports the interpretation of experimental EMGs detected from muscles with oblique architecture. EMGs were recorded from the medial gastrocnemius muscle during voluntary and electrically elicited contractions. Preliminary indications obtained from simulated and experimental signals concern the spatial localization of surface potentials and the myoelectric fatigue. Specifically, the spatial distribution of surface EMGs was localized about the fibers superficial extremity. Strikingly, this localization increased with the pinnation angle, both for the simulated EMGs and the recorded M-waves. Moreover, the average rectified value (ARV) and the mean frequency (MNF) of interference EMGs increased and decreased with simulated fatigue, respectively. The degree of variation in ARV and MNF did not depend on the pinnation angle simulated. Similar variations were observed for the experimental EMGs, although being less evident for a higher fiber inclination. These results are discussed on a physiological context, highlighting the relevance of the model proposed here for the interpretation of gastrocnemius EMGs and for conceiving future experiments on muscles with pinnate geometry.

Evaluation of motor unit placement algorithms for EMG simulation

Motor unit layout algorithms have a significant effect on motor unit fibre densities recorded. Motor unit fibre densities are affected by both the method used to place the motor unit territories, and the mechanism by which muscle fibres are assigned to motor units. The first of these should emulate the process by which separate motor neurons create overlapping territories that cover the muscle cross section, while the second should have some relation to the processes involved with axonal arborization and development of the spatial dispersion of the neuro-muscular junctions. The success of an algorithm in creating physiologically realistic motor unit layouts may be evaluated, in part, by examining the distribution of the muscle fibres assigned to the motor units. This paper examines the motor unit fibre densities found in muscles created by two recent algorithms, and explores the degree to which the concepts used by these algorithms may be shared.

Influence of fatigue on the simulated relation between the amplitude of the surface electromyogram and muscle force

A linear relation between surface electromyogram (EMG) amplitude and muscle force is often assumed and used to estimate the contributions of selected muscles to various tasks. In the presence of muscle fatigue, however, changes in the properties of muscle fibre action potentials and motor unit twitch forces can alter the relation between surface EMG amplitude and force. A novel integrative model of motor neuron control and the generation of muscle fibre action potentials was used to simulate surface EMG signals and muscle force during three fatigue protocols. The change in the simulated relation between surface EMG amplitude and force depended on both the level of fatigue and the details of the fatiguing contraction. In general, surface EMG amplitude overestimated muscle force when fatigue was present. For example, surface EMG amplitudes corresponding to 60 per cent of the amplitude obtained at maximal force without fatigue corresponded to forces in the range 10-40% of the maximal force across three representative fatigue protocols. The results indicate that the surface EMG amplitude cannot be used to predict either the level of muscle activation or the magnitude of muscle force when the muscle exhibits any fatigue.

A muscle architecture model offering control over motor unit fiber density distributions

The aim of this study was to develop a muscle architecture model able to account for the observed distributions of innervation ratios and fiber densities of different types of motor units in a muscle. A model algorithm is proposed and mathematically analyzed in order to obtain an inverse procedure that allows, by modification of input parameters, control over the output distributions of motor unit fiber densities. The model's performance was tested with independent data from a glycogen depletion study of the medial gastrocnemius of the rat. Results show that the model accurately reproduces the observed physiological distributions of innervation ratios and fiber densities and their relationships. The reliability and accuracy of the new muscle architecture model developed here can provide more accurate models for the simulation of different electromyographic signals.

A statistical model and simulator for cardiovascular pressure signals

Physiological signal simulators are often used to conduct validation studies of commercially available devices such as oscillometric non-invasive blood pressure (NIBP) monitors. Numerous assessment studies have been conducted using simulators to validate commercial NIBP monitors. While there are several simulators commercially available to evaluate oscillometric NIBP devices, currently there are no simulators designed to validate invasive pressure signal devices. A statistical model and simulator for invasive cardiovascular pressure signals such as arterial blood pressure and intracranial pressure are described. The model incorporates the effects of respiration on pressure signals and can be used to generate synthetic signals with time and frequency domain characteristics matching any desired subject population. Additionally, the way that noise and artefacts typically present in real pressure signals should be modelled is described. The proposed statistical model is a useful tool for validation of algorithms designed to process or analyse biomedical pressure signals to estimate parameters of clinical interest such as the cardiac frequency, heart rate variability, respiratory frequency, and pulse pressure variation in the presence of noise. The model can be used to simulate signals in order to validate commercial devices that process and analyse invasive pressure signals.

Statistical modeling of cardiovascular signals and parameter estimation based on the extended Kalman filter

Cardiovascular signals such as arterial blood pressure (ABP), pulse oximetry (POX), and intracranial pressure (ICP) contain useful information such as heart rate, respiratory rate, and pulse pressure variation (PPV). We present a novel state-space model of cardiovascular signals and describe how it can be used with the extended Kalman filter (EKF) to simultaneously estimate and track many cardiovascular parameters of interest using a unified statistical approach. We analyze data from four databases containing cardiovascular signals and present representative examples intended to illustrate the versatility, accuracy, and robustness of the algorithm. Our results demonstrate the ability of the algorithm to estimate and track several clinically relevant features of cardiovascular signals. We illustrate how the algorithm can be used to elegantly solve several actively researched and clinically significant problems including heart and respiratory rate estimation, artifact removal, pulse morphology characterization, and PPV estimation.

Simulation analysis of interference EMG during fatiguing voluntary contractions. Part I: What do the intramuscular spike amplitude–frequency histograms reflect?

Decline in amplitude of EMG signals and in the rate of counts of intramuscularly recorded spikes during fatigue is often attributed to a progressive reduction of the neural drive only. As a rule, alterations in intracellular action potential (IAP) are not taken into account. To test correctness of the hypothesis, the effect of various discharge frequency patterns as well as changes in IAP shape and muscle fibre propagation velocity (MFPV) on the spike amplitude-frequency histogram of intramuscular interference EMG signals were simulated and analyzed. It was assumed that muscle was composed of four types of motor units (MUs): slow-twitch fatigue resistant, fast-twitch fatigue resistant, fast intermediate, and fast fatigable. MFPV and IAP duration at initial stage before fatigue as well as their changes differed for individual MU types. Fatigability of individual MU types in normal conditions as well as in the case of ischaemic or low oxygen conditions due to restricted blood flow was also taken into account. It was found that spike amplitude-frequency histogram is poorly sensitive to MU firing frequency, while it is highly sensitive to IAP profile lengthening. It is concluded that spike amplitude-frequency analysis can hardly provide a correct measure of MU rate-coding pattern during fatigue.

Teager–Kaiser Energy Operation of Surface EMG Improves Muscle Activity Onset Detection

This study presents a novel method for detection of the onset time of muscle activity using surface electromyogram (EMG) signals. The method takes advantage of the nonlinear properties of the Teager-Kaiser energy (TKE) operator, which simultaneously considers the amplitude and instantaneous frequency of the surface EMG, and therefore increases the prospects of muscle activity detection. To detect the onset time of muscle activity, the surface EMG signal was first processed by the TKE operator to highlight motor unit activities of the muscle. Then a robust threshold-based algorithm was developed in the TKE domain to locate the onset of muscle activity. The validity of the proposed method was illustrated using various surface EMG simulations as well as experimental surface EMG recordings.

Muscle activity onset time detection using teager-kaiser energy operator

This study presents a novel method, characterized with simple implementation, to automatically detect the onset time of muscle activity using surface electromyogram (EMG) signals. The method applied Teager-Kaiser energy operator (TKEO) to highlight the motor unit activities by simultaneously considering the amplitude and frequency information of the surface EMG, and therefore increase the prospects of muscle activity detection. A robust threshold-based algorithm was then applied to the TKEO output to locate the onset time of muscle activity. The validity of the proposed method was illustrated using both experimental surface EMG recordings and various surface EMG simulations.

Number of sources uncertainty in blind source separation. Application to EMG signal processing

This contribution deals with the number of components uncertainty in blind source separation. The number of components is estimated by maximizing its marginal a posteriori probability which favors the simplest explanation of the observed data. Marginalizing (integrating over all the parameters) is implemented through the Laplace approximation based on an efficient wavelet spectral matching separating algorithm. The effectiveness of the proposed method is shown on EMG data processing.

Simulation of Surface EMG Signals for a Multilayer Volume Conductor With Triangular Model of the Muscle Tissue

This study analytically describes surface electromyogram (sEMG) signals generated by a model of a triangular muscle, i.e., a muscle with fibers arranged in a fan shape. Examples of triangular muscles in the human body are the deltoid, the pectoralis major, the trapezius, the adductor pollicis. A model of triangular muscle is proposed. It is a sector of a cylindrical volume conductor (with the fibers directed along the radial coordinate) bounded at the muscle/fat interface. The muscle conductivity tensor reflects the fan anisotropy. Edge effects have been neglected. A solution of the nonspace invariant problem for a triangular muscle is provided in the Fourier domain. An approximate analytical solution for a two plane layer volume conductor model is obtained by introducing a homogeneous layer (modeling the fat) over the triangular muscle. The results are implemented in a complete sEMG generation model (including the finite length of the fibers), simulating single fiber action potentials. The model is not space invariant due to the changes of the volume conductor along the direction of action potential propagation. Thus the detected potentials at the skin surface change shape as they propagate. This determines problems in the extraction and interpretation of parameters. As a representative example of application of the simulation model, the influence of the inhomogeneity of the volume conductor in conduction velocity (CV) estimation is addressed (for two channels; maximum likelihood and reference point methods). Different fiber depths, electrode placements and small misalignments of the detection system with respect to the fiber have been simulated. The error in CV estimation is large when the depth of the fiber increases, when the detection system is not aligned with the fiber and close to the innervation point and to the tendons.

Simulation analysis of interference EMG during fatiguing voluntary contractions. Part II--changes in amplitude and spectral characteristics

Capabilities of amplitude and spectral methods for information extraction from interference EMG signals were assessed through simulation and preliminary experiment. Muscle was composed of 4 types of motor units (MUs). Different hypotheses on changes in firing frequency of individual MUs, intracellular action potential (IAP) and muscle fibre propagation velocity (MFPV) during fatigue were analyzed. It was found that changes in amplitude characteristics of interference signals (root mean square, RMS, or integrated rectified value, IEMG) detected by intramuscular and surface electrodes differed. RMS and IEMG of surface detected interference signals could increase even under MU firing rate reduction and without MU synchronisation. IAP profile lengthening can affect amplitude characteristics more significantly than MU firing frequency. Thus, an increase of interference EMG amplitude is unreliable to reflect changes in the neural drive. The ratio between EMG amplitude and contraction response can hardly characterise the so-called 'neuromuscular efficiency'. The recently proposed spectral fatigue indices can be used for quantification of interference EMG signals. The indices are practically insensitive to MU firing frequency. IAP profile lengthening and decrease in MFPV enhanced the index value, while recruitment of fast fatigable MUs reduced it. Sensitivity of the indices was higher than that of indices traditionally used.

Behaviour of motor unit action potential rate, estimated from surface EMG, as a measure of muscle activation level

Background

Surface electromyography (EMG) parameters such as root-mean-square value (RMS) are commonly used to assess the muscle activation level that is imposed by the central nervous system (CNS). However, RMS is influenced not only by motor control aspects, but also by peripheral properties of the muscle and recording setup. To assess motor control separately, the number of motor unit action potentials (MUAPs) per second, or MUAP Rate (MR) is a potentially useful measure. MR is the sum of the firing rates of the contributing MUs and as such reflects the two parameters that the CNS uses for motor control: number of MUs and firing rate.

MR can be estimated from multi-channel surface EMG recordings. The objective of this study was to explore the behaviour of estimated MR (eMR) in relation to number of active MUs and firing rate. Furthermore, the influence of parameters related to peripheral muscle properties and recording setup (number of fibers per MU, fiber diameter, thickness of the subcutaneous layer, signal-to-noise-ratio) on eMR was compared with their influence on RMS.

Methods

Physiological parameters were varied in a simulation model that generated multi-channel EMG signals. The behaviour of eMR in simulated conditions was compared with its behaviour in experimental conditions. Experimental data was obtained from the upper trapezius muscle during a shoulder elevation task (20–100 N).

Results

The simulations showed strong, monotonously increasing relations between eMR and number of active MUs and firing rate (r² > 0.95). Because of unrecognized superimpositions of MUAPs, eMR was substantially lower than the actual MUAP Rate (aMR). The percentage of detected MUAPs decreased with aMR, but the relation between eMR and aMR was rather stable in all simulated conditions. In contrast to RMS, eMR was not affected by number of fibers per MU, fiber diameter and thickness of the subcutaneous layer. Experimental data showed a strong relation between eMR and force (individual second order polynomial regression: 0.96 < r² < 0.99).

Conclusion

Although the actual number of MUAPs in the signal cannot be accurately extracted with the present method, the stability of the relation between eMR and aMR and its independence of muscle properties make eMR a suitable parameter to assess the input from the CNS to the muscle at low contraction levels non-invasively.

Motor unit conduction velocity distribution estimation from evoked motor responses

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

A Finite Element Model for Describing the Effect of Muscle Shortening on Surface EMG

A finite-element model for the generation of single fiber action potentials in a muscle undergoing various degrees of fiber shortening is developed. The muscle is assumed fusiform with muscle fibers following a curvilinear path described by a Gaussian function. Different degrees of fiber shortening are simulated by changing the parameters of the fiber path and maintaining the volume of the muscle constant. The conductivity tensor is adapted to the muscle fiber orientation. In each point of the volume conductor, the conductivity of the muscle tissue in the direction of the fiber is larger than that in the transversal direction. Thus, the conductivity tensor changes point-by-point with fiber shortening, adapting to the fiber paths. An analytical derivation of the conductivity tensor is provided. The volume conductor is then studied with a finite-element approach using the analytically derived conductivity tensor. Representative simulations of single fiber action potentials with the muscle at different degrees of shortening are presented. It is shown that the geometrical changes in the muscle, which imply changes in the conductivity tensor, determine important variations in action potential shape, thus affecting its amplitude and frequency content. The model provides a new tool for interpreting surface EMG signal features with changes in muscle geometry, as it happens during dynamic contractions.

Techniques of EMG signal analysis: detection, processing, classification and applications

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.

Classification of surface EMG signal using relative wavelet packet energy

Features can be classified into interferential features and discriminable features according to their contribution to pattern recognition. In this paper, a novel and simple method based on wavelet packet transform is proposed to extract the features from surface EMG signal. In this method, the features are relative wavelet packet energy (RWPE), which is evaluated from several selected frequency bands of surface EMG signal. Compared with a conventional method, which is of the best performance in previous applications, the method can compress the interferential features and enhance the discriminable features more effectively. In consequence, the RWPE features calculated by the method represent different patterns of surface EMG signal more accurately and the accuracy of surface EMG signal pattern classification is improved greatly.

A simulation model of the surface EMG signal for analysis of muscle activity during the gait cycle

This work describes a model able to synthetize the surface EMG (electromyography) signal acquired from tibialis anterior and gastrocnemious medialis muscles during walking of asymptomatic adult subjects. The model assumes a muscle structure where the volume conductor is represented by multiple layers of anisotropic media. This model originates from analysis of the single fiber action potential characterized by the conduction velocity. The surface EMG of voluntary contraction is calculated by gathering motor unit action potentials estimated by the summation of all activities of muscle fibers assumed to have a uniformly parallel distribution. The parameters related to the gait cycle, such as onset and cessation timings of muscle activation, amplitude of muscle contraction, periods and sequences of motor units' recruitment, are included in the model presented. In addition, the relative positions of the electrodes during gait can also be specified in order to adapt the simulation to the different acquisition settings.

A mathematical analysis of SFAP convolutional models

In this paper we compare, from a mathematical point of view, two well-recognized single fiber action potential (SFAP) convolutional models: the Nandedkar-Stalberg (N-S) model and the Dimitrov-Dimitrova (D-D) model. Junction waves appear in N-S SFAPs due to the onset and extinction of the monopoles whereas in D-D SFAPs these waves appear only when the dipoles reach the fiber/tendon junctions. D-D junction waves model more accurately the out-of-the-main-spike waveforms that appear in experimental SFAPs. The origin of junction waves lies in the discontinuities of the impulse responses. There are two kinds of these waves caused by the two types of existing discontinuities (in the impulse response function and in its derivative). We model each kind of discontinuity with a different mathematical function. Using these functions, the N-S and D-D impulse responses can be split and, therefore, the junction waves can be separated from the spike component of the SFAP. The expansion of the impulse response helps us to understand the differences between the N-S and D-D junction waves.