A novel approach for precise simulation of the EMG signal detected by surface electrodes

A novel procedure to automate the removal of PLI and motion artifacts using mode decomposition to enhance pattern recognition of sEMG signals for myoelectric control of prosthesis

Hand Movement Recognition (HMR) with sEMG is crucial for artificial hand prostheses. HMR performance mostly depends on the feature information that is fed to the classifiers. However, sEMG often captures noise like power line interference (PLI) and motion artifacts. This may extract redundant and insignificant feature information, which can degrade HMR performance and increase computational complexity. This study aims to address these issues by proposing a novel procedure for automatically removing PLI and motion artifacts from experimental sEMG signals. This will make it possible to extract better features from the signal and improve the categorization of various hand movements. Empirical mode decomposition and energy entropy thresholding are utilized to select relevant mode components for artifact removal. Time domain features are then used to train classifiers (kNN, LDA, SVM) for hand movement categorization, achieving average accuracies of 92.36%, 93.63%, and 98.12%, respectively, across subjects. Additionally, muscle contraction efforts are classified into low, medium, and high categories using this technique. Validation is performed on data from ten subjects performing eight hand movement classes and three muscle contraction efforts with three surface electrode channels. Results indicate that the proposed preprocessing improves average accuracy by 9.55% with the SVM classifier, significantly reducing computational time.

Implications of EMG channel count: enhancing pattern recognition online prosthetic testing

Introduction

Myoelectric pattern recognition systems have shown promising control of upper limb powered prostheses and are now commercially available. These pattern recognition systems typically record from up to 8 muscle sites, whereas other control systems use two-site control. While previous offline studies have shown 8 or fewer sites to be optimal, real-time control was not evaluated.

Methods

Six individuals with no limb absence and four individuals with a transradial amputation controlled a virtual upper limb prosthesis using pattern recognition control with 8 and 16 channels of EMG. Additionally, two of the individuals with a transradial amputation performed the Assessment for Capacity of Myoelectric Control (ACMC) with a multi-articulating hand and wrist prosthesis with the same channel count conditions.

Results

Users had significant improvements in control when using 16 compared to 8 EMG channels including decreased classification error (p = 0.006), decreased completion time (p = 0.019), and increased path efficiency (p = 0.013) when controlling a virtual prosthesis. ACMC scores increased by more than three times the minimal detectable change from the 8 to the 16-channel condition.

Discussion

The results of this study indicate that increasing EMG channel count beyond the clinical standard of 8 channels can benefit myoelectric pattern recognition users.

A computational model of surface electromyography signal alterations after spinal cord injury

Objective. Spinal cord injury (SCI) can cause significant impairment and disability with an impact on the quality of life for individuals with SCI and their caregivers. Surface electromyography (sEMG) is a sensitive and non-invasive technique to measure muscle activity and has demonstrated great potential in capturing neuromuscular changes resulting from SCI. The mechanisms of the sEMG signal characteristic changes due to SCI are multi-faceted and difficult to studyin vivo. In this study, we utilized well-established computational models to characterize changes in sEMG signal after SCI and identify sEMG features that are sensitive and specific to different aspects of the SCI.Approach. Starting from existing models for motor neuron pool organization and motor unit action potential generation for healthy neuromuscular systems, we implemented scenarios to model damages to upper motor neurons, lower motor neurons, and the number of muscle fibers within each motor unit. After simulating sEMG signals from each scenario, we extracted time and frequency domain features and investigated the impact of SCI disruptions on sEMG features using the Kendall Rank Correlation analysis.Main results. The commonly used amplitude-based sEMG features (such as mean absolute values and root mean square) cannot differentiate between injury scenarios, but a broader set of features (including autoregression and cepstrum coefficients) provides greater specificity to the type of damage present.Significance. We introduce a novel approach to mechanistically relate sEMG features (often underused in SCI research) to different types of neuromuscular alterations that may occur after SCI. This work contributes to the further understanding and utilization of sEMG in clinical applications, which will ultimately improve patient outcomes after SCI.

The Sensitivity of Bipolar Electromyograms to Muscle Excitation Scales With the Inter-Electrode Distance

The value of surface electromyograms (EMGs) lies in their potential to non-invasively probe the neuromuscular system. Whether muscle excitation may be accurately inferred from bipolar EMGs depends on how much the detected signal is both sensitive and specific to the excitation of the target muscle. While both are known to be a function of the inter-electrode distance (IED), specificity has been of long concern in the physiological literature. In contrast, sensitivity, at best, has been implicitly assumed. Here we provide evidence that the IED imposes a biophysical constraint on the sensitivity of surface EMG. From 20 healthy subjects, we tested the hypothesis that excessively reducing the IED limits EMGs' physiological content. We detected bipolar EMGs with IEDs varying from 5 mm to 50 mm from two skeletal muscles with distinct architectures, gastrocnemius and biceps brachii. Non-parametric statistics and Bayesian hierarchical modelling were used to evaluate the dependence of the onset of muscle excitation and signal-to-noise ratio (SNR) on the IED. Experimental results revealed that IED critically affects the sensitivity of bipolar EMGs for both muscles-indeliberately reducing the IED yields EMGs that are not representative of the whole muscle, hampering validity. Simulation results substantiate the generalization of experimental results to small and large electrodes. Based on current and previous findings, we discuss a potentially valid procedure for defining the most appropriate IED for a single bipolar, surface recording-i.e., the distance from the electrode to the target muscle boundary may heuristically serve as a lower bound when choosing an IED.

Tutorial. Surface electromyogram (sEMG) amplitude estimation: Best practices

This tutorial intends to provide insight, instructions and "best practices" for those who are novices-including clinicians, engineers and non-engineers-in extracting electromyogram (EMG) amplitude from the bipolar surface EMG (sEMG) signal of voluntary contractions. A brief discussion of sEMG amplitude extraction from high density sEMG (HDsEMG) arrays and feature extraction from electrically elicited contractions is also provided. This tutorial attempts to present its main concepts in a straightforward manner that is accessible to novices in the field not possessing a wide range of technical background (if any) in this area. Surface EMG amplitude, also referred to as the sEMG envelope [often implemented as root mean square (RMS) sEMG or average rectified value (ARV) sEMG], quantifies the voltage variation of the sEMG signal and is grossly related to the overall neural excitation of the muscle and to peripheral parameters. The tutorial briefly reviews the physiological origin of the voluntary sEMG signal and sEMG recording, including electrode configurations, sEMG signal transduction, electronic conditioning and conversion by an analog-to-digital converter. These topics have been covered in greater detail in prior tutorials in this series. In depth descriptions of state-of-the-art methods for computing sEMG amplitude are then provided, including guidance on signal pre-conditioning, absolute value vs. square-law detection, selection of appropriate sEMG amplitude smoothing filters and attenuation of measurement noise. The tutorial provides a detailed list of best practices for sEMG amplitude estimation.

A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model

Motion predictions for limbs can be performed using commonly called Hill-based muscle models. For this type of models, a surface electromyogram (sEMG) of the muscle serves as an input signal for the activation of the muscle model. However, the Hill model needs additional information about the mechanical system state of the muscle (current length, velocity, etc.) for a reliable prediction of the muscle force generation and, hence, the prediction of the joint motion. One feature that contains potential information about the state of the muscle is the position of the center of the innervation zone. This feature can be further extracted from the sEMG. To find the center, a wavelet-based algorithm is proposed that localizes motor unit potentials in the individual channels of a single-column sEMG array and then identifies innervation point candidates. In the final step, these innervation point candidates are clustered in a density-based manner. The center of the largest cluster is the estimated center of the innervation zone. The algorithm has been tested in a simulation. For this purpose, an sEMG simulator was developed and implemented that can compute large motor units (1,000's of muscle fibers) quickly (within seconds on a standard PC).

Forearm sEMG data from young healthy humans during the execution of hand movements

This work provides a complete dataset containing surface electromyography (sEMG) signals acquired from the forearm with a sampling frequency of 1000 Hz. The dataset is named WyoFlex sEMG Hand Gesture and recorded the data of 28 participants between 18 and 37 years old without neuromuscular diseases or cardiovascular problems. The test protocol consisted of sEMG signals acquisition corresponding to ten wrist and grasping movements (extension, flexion, ulnar deviation, radial deviation, hook grip, power grip, spherical grip, precision grip, lateral grip, and pinch grip), considering three repetitions for each gesture. Also, the dataset contains general information such as anthropometric measures of the upper limb, gender, age, laterally of the person, and physical condition. Likewise, the implemented acquisition system consists of a portable armband with four sEMG channels distributed equidistantly for each forearm. The database could be used for the recognition of hand gestures, evaluation of the evolution of patients in rehabilitation processes, control of upper limb orthoses or prostheses, and biomechanical analysis of the forearm.

Current developments in surface electromyography

Background/aim

Surface electromyography (surface EMG) is a primary technique to detect the electrical activities of muscles through surface electrodes. In recent years, surface EMG applications have grown from conventional fields into new fields. However, there is a gap between the progress in the research of surface EMG and its clinical acceptance, characterized by the translational knowledge and skills in the widespread use of surface EMG among the clinician community. To reduce this gap, it is necessary to translate the updated surface EMG applications and technological advances into clinical research. Therefore, we aimed to present a perspective on recent developments in the application of surface EMG and signal processing methods.

Materials and methods

We conducted this scoping review following the Joanna Briggs Institute (JBI) method. We conducted a general search of PubMed and Web of Science to identify key search terms. Following the search, we uploaded selected articles into Rayyan and removed duplicates. After prescreening 133 titles and abstracts, we assessed 91 full texts according to the inclusion criteria.

Results

We concluded that surface EMG has made innovative technological progress and has research potential for routine clinical applications and a wide range of applications, such as neurophysiology, sports and art performances, biofeedback, physical therapy and rehabilitation, assessment of physical exercises, muscle strength, fatigue, posture and postural control, movement analysis, muscle coordination, motor synergies, modelling, and more. Novel methods have been applied for surface EMG signals in terms of time domain, frequency domain, time-frequency domain, statistical methods, and nonlinear methods.

Conclusion

Translating innovations in surface EMG and signal analysis methods into routine clinical applications can be a helpful tool with a growing and valuable role in muscle activation measurement in clinical practices. Thus, researchers must build many more interfaces that give opportunities for continuing education and research with more contemporary techniques and devices.

A myoelectric digital twin for fast and realistic modelling in deep learning

Muscle electrophysiology has emerged as a powerful tool to drive human machine interfaces, with many new recent applications outside the traditional clinical domains, such as robotics and virtual reality. However, more sophisticated, functional, and robust decoding algorithms are required to meet the fine control requirements of these applications. Deep learning has shown high potential in meeting these demands, but requires a large amount of high-quality annotated data, which is expensive and time-consuming to acquire. Data augmentation using simulations, a strategy applied in other deep learning applications, has never been attempted in electromyography due to the absence of computationally efficient models. We introduce a concept of Myoelectric Digital Twin - highly realistic and fast computational model tailored for the training of deep learning algorithms. It enables simulation of arbitrary large and perfectly annotated datasets of realistic electromyography signals, allowing new approaches to muscular signal decoding, accelerating the development of human-machine interfaces.

Ergonomic human-robot collaboration in industry: A review

In the current industrial context, the importance of assessing and improving workers' health conditions is widely recognised. Both physical and psycho-social factors contribute to jeopardising the underlying comfort and well-being, boosting the occurrence of diseases and injuries, and affecting their quality of life. Human-robot interaction and collaboration frameworks stand out among the possible solutions to prevent and mitigate workplace risk factors. The increasingly advanced control strategies and planning schemes featured by collaborative robots have the potential to foster fruitful and efficient coordination during the execution of hybrid tasks, by meeting their human counterparts' needs and limits. To this end, a thorough and comprehensive evaluation of an individual's ergonomics, i.e. direct effect of workload on the human psycho-physical state, must be taken into account. In this review article, we provide an overview of the existing ergonomics assessment tools as well as the available monitoring technologies to drive and adapt a collaborative robot's behaviour. Preliminary attempts of ergonomic human-robot collaboration frameworks are presented next, discussing state-of-the-art limitations and challenges. Future trends and promising themes are finally highlighted, aiming to promote safety, health, and equality in worldwide workplaces.

Blind source separation of inspiration and expiration in respiratory sEMG signals

OBJECTIVE

Surface electromyography (sEMG) is a noninvasive option for monitoring respiratory effort in ventilated patients. However, respiratory sEMG signals are affected by crosstalk and cardiac activity. This work addresses the blind source separation (BSS) of inspiratory and expiratory electrical activity in single- or two-channel recordings. The main contribution of the presented methodology is its applicability to the addressed muscles and the number of available channels.

APPROACH

We propose a two-step procedure consisting of a single-channel cardiac artifact removal algorithm, followed by a single- or multi-channel BSS stage. First, cardiac components are removed in the wavelet domain. Subsequently, a nonnegative matrix factorization (NMF) algorithm is applied to the envelopes of the resulting wavelet bands. The NMF is initialized based on simultaneous standard pneumatic measurements of the ventilated patient.

MAIN RESULTS

The proposed estimation scheme is applied to twelve clinical datasets and simulated sEMG signals of the respiratory system. The results on the clinical datasets are validated based on expert annotations using invasive pneumatic measurements. In the simulation, three measures evaluate the separation success: The distortion and the correlation to the known ground truth and the inspiratory-to-expiratory signal power ratio. We find an improvement across all SNRs, recruitment patterns, and channel configurations. Moreover, our results indicate that the initialization strategy replaces the manual matching of sources after the BSS.

SIGNIFICANCE

The proposed separation algorithm facilitates the interpretation of respiratory sEMG signals. In crosstalk affected measurements, the developed method may help clinicians distinguish between inspiratory effort and other muscle activities using only noninvasive measurements.

Endurance-exercise training adaptations in spinal motoneurones: potential functional relevance to locomotor output and assessment in humans

It is clear from non-human animal work that spinal motoneurones undergo endurance training (chronic) and locomotor (acute) related changes in their electrical properties and thus their ability to fire action potentials in response to synaptic input. The functional implications of these changes, however, are speculative. In humans, data suggests that similar chronic and acute changes in motoneurone excitability may occur, though the work is limited due to technical constraints. To examine the potential influence of chronic changes in human motoneurone excitability on the acute changes that occur during locomotor output, we must develop more sophisticated recording techniques or adapt our current methods. In this review, we briefly discuss chronic and acute changes in motoneurone excitability arising from non-human and human work. We then discuss the potential interaction effects of chronic and acute changes in motoneurone excitability and the potential impact on locomotor output. Finally, we discuss the use of high-density surface electromyogram recordings to examine human motor unit firing patterns and thus, indirectly, motoneurone excitability. The assessment of single motor units from high-density recording is mainly limited to tonic motor outputs and minimally dynamic motor output such as postural sway. Adapting this technology for use during locomotor outputs would allow us to gain a better understanding of the potential functional implications of endurance training-induced changes in human motoneurone excitability on motor output.

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.

Investigation of the temporal and spatial dynamics of muscular action potentials through optically pumped magnetometers

AIM

This study aims to simultaneously record the magnetic and electric components of the propagating muscular action potential.

METHOD

A single-subject study of the monosynaptic stretch reflex of the musculus rectus femoris was performed; the magnetic field generated by the muscular activity was recorded in all three spatial directions by five optically pumped magnetometers. In addition, the electric field was recorded by four invasive fine-wire needle electrodes. The magnetic and electric fields were compared by modelling the muscular anatomy of the rectus femoris muscle and by simulating the corresponding magnetic field vectors.

RESULTS

The magnetomyography (MMG) signal can reliably be recorded following the stimulation of the monosynaptic stretch reflex. The MMG signal shows several phases of activity inside the muscle, the first of which is the propagating muscular action potential. As predicted by the finite wire model, the magnetic field vectors of the propagating muscular action potential are generated by the current flowing along the muscle fiber. Based on the magnetic field vectors, it was possible to reconstruct the pinnation angle of the muscle fibers. The later magnetic field components are linked to the activation of the contractile apparatus. Interpretation MMG allows to analyze the muscle physiology from the propagating muscular action potential to the initiation of the contractile apparatus. At the same time, this methods reveals information about muscle fiber direction and extend. With the development of high-resolution magnetic cameras, that are based on OPM technology, it will be possible to image the function and structure of the biomagnetic field of any skeletal muscle with high precision. This method could be used both, in clinical medicine and also in sports science.

Only the Fastest Corticospinal Fibers Contribute to β Corticomuscular Coherence

Human corticospinal transmission is commonly studied using brain stimulation. However, this approach is biased to activity in the fastest conducting axons. It is unclear whether conclusions obtained in this context are representative of volitional activity in mild-to-moderate contractions. An alternative to overcome this limitation may be to study the corticospinal transmission of endogenously generated brain activity. Here, we investigate in humans (N = 19; of either sex), the transmission speeds of cortical β rhythms (∼20 Hz) traveling to arm (first dorsal interosseous) and leg (tibialis anterior; TA) muscles during tonic mild contractions. For this purpose, we propose two improvements for the estimation of corticomuscular β transmission delays. First, we show that the cumulant density (cross-covariance) is more accurate than the commonly-used directed coherence to estimate transmission delays in bidirectional systems transmitting band-limited signals. Second, we show that when spiking motor unit activity is used instead of interference electromyography, corticomuscular transmission delay estimates are unaffected by the shapes of the motor unit action potentials (MUAPs). Applying these improvements, we show that descending corticomuscular β transmission is only 1-2 ms slower than expected from the fastest corticospinal pathways. In the last part of our work, we show results from simulations using estimated distributions of the conduction velocities for descending axons projecting to lower motoneurons (from macaque histologic measurements) to suggest two scenarios that can explain fast corticomuscular transmission: either only the fastest corticospinal axons selectively transmit β activity, or else the entire pool does. The implications of these two scenarios for our understanding of corticomuscular interactions are discussed.SIGNIFICANCE STATEMENT We present and validate an improved methodology to measure the delay in the transmission of cortical β activity to tonically-active muscles. The estimated corticomuscular β transmission delays obtained with this approach are remarkably similar to those expected from transmission in the fastest corticospinal axons. A simulation of β transmission along a pool of corticospinal axons using an estimated distribution of fiber diameters suggests two possible mechanisms by which fast corticomuscular transmission is achieved: either a very small fraction of the fastest descending axons transmits β activity to the muscles or, alternatively, the entire population does and natural cancellation of slow channels occurs because of the distribution of axon diameters in the corticospinal tract.

A Neuromotor to Acoustical Jaw-Tongue Projection Model With Application in Parkinson's Disease Hypokinetic Dysarthria

Aim

The present work proposes the study of the neuromotor activity of the masseter-jaw-tongue articulation during diadochokinetic exercising to establish functional statistical relationships between surface Electromyography (sEMG), 3D Accelerometry (3DAcc), and acoustic features extracted from the speech signal, with the aim of characterizing Hypokinetic Dysarthria (HD). A database of multi-trait signals of recordings from an age-matched control and PD participants are used in the experimental study.

Hypothesis:

The main assumption is that information between sEMG and 3D acceleration, and acoustic features may be quantified using linear regression methods.

Methods

Recordings from a cohort of eight age-matched control participants (4 males, 4 females) and eight PD participants (4 males, 4 females) were collected during the utterance of a diadochokinetic exercise (the fast repetition of diphthong [aI]). The dynamic and acoustic absolute kinematic velocities produced during the exercises were estimated by acoustic filter inversion and numerical integration and differentiation of the speech signal. The amplitude distributions of the absolute kinematic and acoustic velocities (AKV and AFV) are estimated to allow comparisons in terms of Mutual Information.

Results

The regression results show the relationships between sEMG and dynamic and acoustic estimates. The projection methodology may help in understanding the basic neuromotor muscle activity regarding neurodegenerative speech in remote monitoring neuromotor and neurocognitive diseases using speech as the vehicular tool, and in the study of other speech-related disorders. The study also showed strong and significant cross-correlations between articulation kinematics, both for the control and the PD cohorts. The absolute kinematic variables presents an observable difference for the PD participants compared to the control group.

Conclusion

Kinematic distributions derived from acoustic analysis may be useful biomarkers toward characterizing HD in neuromotor disorders providing new insights into PD.

Effects of detection system parameters on cross-correlations between MUAPs generated from parallel and inclined muscle fibres

The aim of this study was to investigate the effects of inter-electrode distance (IED), electrode radius (ER) and electrodes configurations on cross-correlation coefficient (CC) between motor unit action potentials (MUAPs) generated in a motor unit (MU) of parallel fibres and in a MU of inclined fibres with respect to the detection system. The fibres inclination angle (FIA) varied from 0° to 180° by a step of 5°. Six spatial filters (the longitudinal single differential (LSD), longitudinal double differential (LDD), bi-transversal double differential (BiTDD), normal double differential (NDD), an inverse binomial filter of order two (IB2) and maximum kurtosis filter (MKF)), three values of IED and three values of ER were considered.

A cylindrical multilayer volume conductor constituted by bone, muscle, fat and skin layers was used to simulate the MUAPs.

The cross-correlation coefficient analysis showed that with the increase of the FIA, the pairs of MUAPs detected by the IB2 system were more correlated than those detected by the five other systems. For each FIA, the findings also showed that the MUAPs pairs detected by BiTDD, NDD, IB2 and MKF systems were more correlated with smaller IEDs than with larger ones, while inverse results were found with the LSD and LDD systems. In addition, the pairs of MUAPs detected by the LDD, BiTDD, IB2 and MKF systems were more correlated with large ERs than with smaller ones. However, inverse results were found with the LSD and NDD systems.

Scalogram-energy based segmentation of surface electromyography signals from swallowing related muscles

BACKGROUND AND OBJECTIVE

The swallowing is a complex process mediated by the central nervous system, that implies voluntary and involuntary components, including 26 pairs of muscles. Non-invasive strategies, including the surface electromyography (sEMG), have been proposed to evaluate the swallowing. However, such analyses have been mostly descriptive, and the detection of neuromuscular activity has been limited to the visual inspection (VIS). Nonetheless, the VIS lacks reliability since the swallowing related muscles have small size, they are not completely shallow, suffer from cross-talk and have low signal-to-noise ratio (SNR). In this way, we propose a wavelet based method to automatically detect activations in sEMG signals acquired during praxis and swallowing tasks.

METHODS

The proposed strategy, namely Scalogram-Energy based Segmentation method, was applied on sEMG signals recorded in masseteric, orbicular, supra- and infrahyoid muscles. The method was trained in a database of 35 healthy subjects by the use of multi-objective genetic algorithms and tested via cross-validation, aiming to maximize the F₁ score and minimize the timing error between the automatic and VIS related marks. Furthermore, the proposed method was tested in a database of semi-synthetic signals with variable SNR built from signals collected from 10 individuals. Additionally, the method was compared with a double threshold based algorithm as well as with other based on energy and morphological operators.

RESULTS

The algorithm achieved a F₁ score of 0.82 and almost 13 ms of error in the estimation of onset and offset. Afterwards, we applied the optimized algorithm to a set with semi-synthetic signals with variable SNR, that achieved F₁ score of 0.85 for SNR=6 dB and 0.97 for SNR=8 and 10 dB. The mean of the timing error was smaller than 9 ms for SNR=6,8 and 10 dB. The method was also compared with a double threshold based algorithm as well as with other based on energy and morphological operators.

CONCLUSIONS

The proposed method shown to be useful to automatically analyze the electrophysiological activity associated to praxis and swallowing process. Nonetheless, the obtained results could be extended to other sEMG related applications.

Control of Spinal Motoneurons by Feedback From a Non-Invasive Real-Time Interface

Interfacing with human neural cells during natural tasks provides the means for investigating the working principles of the central nervous system and for developing human-machine interaction technologies. Here we present a computationally efficient non-invasive, real-time interface based on the decoding of the activity of spinal motoneurons from wearable high-density electromyogram (EMG) sensors. We validate this interface by comparing its decoding results with those obtained with invasive EMG sensors and offline decoding, as reference. Moreover, we test the interface in a series of studies involving real-time feedback on the behavior of a relatively large number of decoded motoneurons. The results on accuracy, intuitiveness, and stability of control demonstrate the possibility of establishing a direct non-invasive interface with the human spinal cord without the need for extensive training. Moreover, in a control task, we show that the accuracy in control of the proposed neural interface may approach that of the natural control of force. These results are the first that demonstrate the feasibility and validity of a non-invasive direct neural interface with the spinal cord, with wearable systems and matching the neural information flow of natural movements.

Multi-Channel Surface EMG Spatio-Temporal Image Enhancement Using Multi-Scale Hessian-Based Filters

Surface electromyography (sEMG) signals acquired with linear electrode array are useful in analyzing muscle anatomy and physiology. Most algorithms for signal processing, detection, and estimation require adequate quality of the input signals, however, multi-channel sEMG signals are commonly contaminated due to several noise sources. The sEMG signal needs to be enhanced prior to the digital signal and image processing to achieve the best results. This study is using spatio-temporal images to represent surface EMG signals. The motor unit action potential (MUAP) in these images looks like a linear structure, making certain angles with the x-axis, depending on the conduction velocity of the MU. A multi-scale Hessian-based filter is used to enhance the linear structure, i.e., the MUAP region, and to suppress the background noise. The proposed framework is compared with some of the existing algorithms using synthetic, simulated, and experimental sEMG signals. Results show improved detection accuracy of the motor unit action potential after the proposed enhancement as a preprocessing step.

Real-Time Quality Assessment of Long-Term ECG Signals Recorded by Wearables in Free-Living Conditions

OBJECTIVE

Nowadays, methods for ECG quality assessment are mostly designed to binary distinguish between good/bad quality of the whole signal. Such classification is not suitable to long-term data collected by wearable devices. In this paper, a novel approach to estimate long-term ECG signal quality is proposed.

METHODS

The real-time quality estimation is performed in a local time window by calculation of continuous signal-to-noise ratio (SNR) curve. The layout of the data quality segments is determined by analysis of SNR waveform. It is distinguished between three levels of ECG signal quality: signal suitable for full wave ECG analysis, signal suitable only for QRS detection, and signal unsuitable for further processing.

RESULTS

The SNR limits for reliable QRS detection and full ECG waveform analysis are 5 and 18 dB respectively. The method was developed and tested using synthetic data and validated on real data from wearable device.

CONCLUSION

The proposed solution is a robust, accurate and computationally efficient algorithm for annotation of ECG signal quality that will facilitate the subsequent tailored analysis of ECG signals recorded in free-living conditions.

SIGNIFICANCE

The field of long-term ECG signals self-monitoring by wearable devices is swiftly developing. The analysis of massive amount of collected data is time consuming. It is advantageous to characterize data quality in advance and thereby limit consequent analysis to useable signals.

A new detection method for EMG activity monitoring

This paper introduces a new approach for electromyography (EMG) activity monitoring based on an improved version of the adaptive linear energy detector (ALED), a widely used technique in voice activity detection. More precisely, we propose a modified ALED technique (named M-ALED) to improve the method's robustness with respect to noise. To achieve this objective, M-ALED relies on the Teager-Kaiser operator for signal pre-conditioning to increase the SNR and uses the order statistics to gain robustness against the signal's impulsiveness. We propose again to exploit the order statistics for the initial signal baseline estimation to deal with the cases where such information is unavailable. Finally, since M-ALED detects the signal's activity at the frame level, we propose in a second stage to refine this detection (at the sample level) by using a constant false alarm rate (CFAR) approach leading to the fine M-ALED (FM-ALED) solution. The performance of FM-ALED is assessed via real and synthetic EMG signal recordings and the obtained results highlight its effectiveness as compared with the state-of-the-art methods (it reduces the mean error probability by a factor close to 2).

Tutorial. Surface EMG detection in space and time: Best practices

This tutorial is aimed to non-engineers using, or planning to use, surface electromyography (sEMG) as an assessment tool in the prevention, monitoring and rehabilitation fields. Its first purpose is to address the issues related to the origin and nature of the signal and to its detection (electrode size, distance, location) by one-dimensional (bipolar and linear arrays) and two-dimensional (grids) electrode systems while avoiding advanced mathematical, physical or physiological issues. Its second purpose is to outline best practices and provide general guidelines for proper signal detection. Issues related to the electrode-skin interface, signal conditioning and interpretation will be discussed in subsequent tutorials.

Modelling the electrical activity of skeletal muscle tissue using a multi-domain approach

Electromyography (EMG) can be used to study the behaviour of the motor neurons and thus provides insights into the physiology of the central nervous system. However, due to the high complexity of neuromuscular control, EMG signals are challenging to interpret. While the exact knowledge of the excitation patterns of a specific muscle within an in vivo experimental setting remains elusive, simulations allow to systematically investigate EMG signals in a controlled environment. Within this context, simulations can provide virtual EMG data, which, for example, can be used to validate and optimise signal analysis methods that aim to estimate the relationship between EMG signals and the output of motor neuron pools. However, since existing methods, which are employed to compute EMG signals, exhibit deficiencies with respect to the physical model itself as well as with respect to numerical aspects, we propose a novel homogenised continuum model that closely resolves the electro-physiological behaviour of skeletal muscle tissue. The proposed model is based on an extension of the well-established bidomain model and includes a biophysically detailed description of the electrical activity within the tissue, which is due to the depolarisation of the muscle fibre membranes. In contrast to all other published EMG models, which assume that the electrical potential field for each muscle fibre can be calculated independently, the proposed model assumes that the electrical potential in the muscle fibres is coupled to the electrical potential in the extracellular space. We show that the newly proposed model is able to simulate realistic EMG signals and demonstrate the potential to employ the predicted virtual EMG signal in order to evaluate the goodness of automated decomposition algorithms.

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.

Multiscale modeling of the neuromuscular system: Coupling neurophysiology and skeletal muscle mechanics

Mathematical models and computer simulations have the great potential to substantially increase our understanding of the biophysical behavior of the neuromuscular system. This, however, requires detailed multiscale, and multiphysics models. Once validated, such models allow systematic in silico investigations that are not necessarily feasible within experiments and, therefore, have the ability to provide valuable insights into the complex interrelations within the healthy system and for pathological conditions. Most of the existing models focus on individual parts of the neuromuscular system and do not consider the neuromuscular system as an integrated physiological system. Hence, the aim of this advanced review is to facilitate the prospective development of detailed biophysical models of the entire neuromuscular system. For this purpose, this review is subdivided into three parts. The first part introduces the key anatomical and physiological aspects of the healthy neuromuscular system necessary for modeling the neuromuscular system. The second part provides an overview on state-of-the-art modeling approaches representing all major components of the neuromuscular system on different time and length scales. Within the last part, a specific multiscale neuromuscular system model is introduced. The integrated system model combines existing models of the motor neuron pool, of the sensory system and of a multiscale model describing the mechanical behavior of skeletal muscles. Since many sub-models are based on strictly biophysical modeling approaches, it closely represents the underlying physiological system and thus could be employed as starting point for further improvements and future developments. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Analytical and Computational Methods > Computational Methods Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models.

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.

Non-invasive Decoding of the Motoneurons: A Guided Source Separation Method Based on Convolution Kernel Compensation With Clustered Initial Points

Despite the progress in understanding of neural codes, the studies of the cortico-muscular coupling still largely rely on interferential electromyographic (EMG) signal or its rectification for the assessment of motor neuron pool behavior. This assessment is non-trivial and should be used with precaution. Direct analysis of neural codes by decomposing the EMG, also known as neural decoding, is an alternative to EMG amplitude estimation. In this study, we propose a fully-deterministic hybrid surface EMG (sEMG) decomposition approach that combines the advantages of both template-based and Blind Source Separation (BSS) decomposition approaches, a.k.a. guided source separation (GSS), to identify motor unit (MU) firing patterns. We use the single-pass density-based clustering algorithm to identify possible cluster representatives in different sEMG channels. These cluster representatives are then used as initial points of modified gradient Convolution Kernel Compensation (gCKC) algorithm. Afterwards, we use the Kalman filter to reduce the noise impact and increase convergence rate of MU filter identification by gCKC. Moreover, we designed an adaptive soft-thresholding method to identify MU firing times out of estimated MU spike trains. We tested the proposed algorithm on a set of synthetic sEMG signals with known MU firing patterns. A grid of 9 × 10 monopolar surface electrodes with 5-mm inter-electrode distances in both directions was simulated. Muscle excitation was set to 10, 30, and 50%. Colored Gaussian zero-mean noise with the signal-to-noise ratio (SNR) of 10, 20, and 30 dB, respectively, was added to 16 s long sEMG signals that were sampled at 4,096 Hz. Overall, 45 simulated signals were analyzed. Our decomposition approach was compared with gCKC algorithm. Overall, in our algorithm, the average numbers of identified MUs and Rate-of-Agreement (RoA) were 16.41 ± 4.18 MUs and 84.00 ± 0.06%, respectively, whereas the gCKC identified 12.10 ± 2.32 MUs with the average RoA of 90.78 ± 0.08%. Therefore, the proposed GSS method identified more MUs than the gCKC, with comparable performance. Its performance was dependent on the signal quality but not the signal complexity at different force levels. The proposed algorithm is a promising new offline tool in clinical neurophysiology.

A Comprehensive Mathematical Model of Motor Unit Pool Organization, Surface Electromyography, and Force Generation

Neuromuscular physiology is a vibrant research field that has recently seen exciting advances. Previous publications have focused on thorough analyses of particular aspects of neuromuscular physiology, yet an integration of the various novel findings into a single, comprehensive model is missing. In this article, we provide a unified description of a comprehensive mathematical model of surface electromyographic (EMG) measurements and the corresponding force signal in skeletal muscles, both consolidating and extending the results of previous studies regarding various components of the neuromuscular system. The model comprises motor unit (MU) pool organization, recruitment and rate coding, intracellular action potential generation and the resulting EMG measurements, as well as the generated muscular force during voluntary isometric contractions. Mathematically, it consists of a large number of linear PDEs, ODEs, and various stochastic nonlinear relationships, some of which are solved analytically, others numerically. A parameterization of the electrical and mechanical components of the model is proposed that ensures a physiologically meaningful EMG-force relation in the simulated signals, in particular taking the continuous, size-dependent distribution of MU parameters into account. Moreover, a novel nonlinear transformation of the common drive model input is proposed, which ensures that the model force output equals the desired target force. On a physiological level, this corresponds to adjusting the rate coding model to the force generating capabilities of the simulated muscle, while from a control theoretic point of view, this step is equivalent to an exact linearizing transformation of the controlled neuromuscular system. Finally, an alternative analytical formulation of the EMG model is proposed, which renders the physiological meaning of the model more clear and facilitates a mathematical proof that muscle fibers in this model at no point in time represent a net current source or sink. A consistent description of a complete physiological model as presented here, including thorough justification of model component choices, will facilitate the use of these advanced models in future research. Results of a numerical simulation highlight the model's capability to reproduce many physiological effects observed in experimental measurements, and to produce realistic synthetic data that are useful for the validation of signal processing algorithms.

Systematic differences of non-invasive dominant frequency estimation compared to invasive dominant frequency estimation in atrial fibrillation

Non-invasive analysis of atrial fibrillation (AF) using body surface mapping (BSM) has gained significant interest, with attempts at interpreting atrial spectro-temporal parameters from body surface signals. As these body surface signals could be affected by properties of the torso volume conductor, this interpretation is not always straightforward. This paper highlights the volume conductor effects and influences of the algorithm parameters for identifying the dominant frequency (DF) from cardiac signals collected simultaneously on the torso and atrial surface. Bi-atrial virtual electrograms (VEGMs) and BSMs were recorded simultaneously for 5 min from 10 patients undergoing ablation for persistent AF. Frequency analysis was performed on 4 s segments. DF was defined as the frequency with highest power between 4 and 10 Hz with and without applying organization index (OI) thresholds. The volume conductor effect was assessed by analyzing the highest DF (HDF) difference of each VEGM HDF against its BSM counterpart. Significant differences in HDF values between intra-cardiac and torso signals could be observed, independent of OI threshold. This difference increases with increasing endocardial HDF (BSM-VEGM median difference from -0.13 Hz for VEGM HDF at 6.25 ± 0.25 Hz to -4.24 Hz at 9.75 ± 0.25 Hz), thereby confirming the theory of the volume conductor effect in real-life situations. Applying an OI threshold strongly affected the BSM HDF area size and location and atrial HDF area location. These results suggest that volume conductor and measurement algorithm effects must be considered for appropriate clinical interpretation.

Enabling Detailed, Biophysics-Based Skeletal Muscle Models on HPC Systems

Realistic simulations of detailed, biophysics-based, multi-scale models often require very high resolution and, thus, large-scale compute facilities. Existing simulation environments, especially for biomedical applications, are typically designed to allow for high flexibility and generality in model development. Flexibility and model development, however, are often a limiting factor for large-scale simulations. Therefore, new models are typically tested and run on small-scale compute facilities. By using a detailed biophysics-based, chemo-electromechanical skeletal muscle model and the international open-source software library OpenCMISS as an example, we present an approach to upgrade an existing muscle simulation framework from a moderately parallel version toward a massively parallel one that scales both in terms of problem size and in terms of the number of parallel processes. For this purpose, we investigate different modeling, algorithmic and implementational aspects. We present improvements addressing both numerical and parallel scalability. In addition, our approach includes a novel visualization environment which is based on the MegaMol framework and is capable of handling large amounts of simulated data. We present the results of a number of scaling studies at the Tier-1 supercomputer HazelHen at the High Performance Computing Center Stuttgart (HLRS). We improve the overall runtime by a factor of up to 2.6 and achieve good scalability on up to 768 cores.

A simulation study on the relation between the motor unit depth and action potential from multi-channel surface electromyography recordings

To investigate the spatial information of individual motor unit (MUs) using multi-channel surface electromyography (EMG) decomposition. The K-means clustering convolution kernel compensation (KmCKC) approach was employed to detect the innervation pulse trains (IPTs) from the simulated surface EMG signals, and the motor unit action potentials (MUAPs) were evaluated using the spike-triggered average (STA) technique. The relationships between the features of MUAP and MU depth were determinated with a least square fitting method. The errors of peak-to-peak (PTP) amplitude of reconstructed MUAPs were less than 5.73%, even with 0 dB signal-to-noise (SNR). The fitting errors with nonlinear model were less than 5.55% for SNRs higher than 20 dB. The results show that it is possible to provide a useful method for estimating MU depth from surface EMG recordings. It is expected to extend the applicability of surface EMG technique to more challenging clinical applications.

Speedup computation of HD-sEMG signals using a motor unit-specific electrical source model

Nowadays, bio-reliable modeling of muscle contraction is becoming more accurate and complex. This increasing complexity induces a significant increase in computation time which prevents the possibility of using this model in certain applications and studies. Accordingly, the aim of this work is to significantly reduce the computation time of high-density surface electromyogram (HD-sEMG) generation. This will be done through a new model of motor unit (MU)-specific electrical source based on the fibers composing the MU. In order to assess the efficiency of this approach, we computed the normalized root mean square error (NRMSE) between several simulations on single generated MU action potential (MUAP) using the usual fiber electrical sources and the MU-specific electrical source. This NRMSE was computed for five different simulation sets wherein hundreds of MUAPs are generated and summed into HD-sEMG signals. The obtained results display less than 2% error on the generated signals compared to the same signals generated with fiber electrical sources. Moreover, the computation time of the HD-sEMG signal generation model is reduced to about 90% compared to the fiber electrical source model. Using this model with MU electrical sources, we can simulate HD-sEMG signals of a physiological muscle (hundreds of MU) in less than an hour on a classical workstation. Graphical Abstract Overview of the simulation of HD-sEMG signals using the fiber scale and the MU scale. Upscaling the electrical source to the MU scale reduces the computation time by 90% inducing only small deviation of the same simulated HD-sEMG signals.

A computational model to investigate the effect of pennation angle on surface electromyogram of Tibialis Anterior

This study has described and experimentally validated the differential electrodes surface electromyography (sEMG) model for tibialis anterior muscles during isometric contraction. This model has investigated the effect of pennation angle on the simulated sEMG signal. The results show that there is no significant effect of pennation angle in the range 0° to 20° to the single fibre action potential shape recorded on the skin surface. However, the changes with respect to pennation angle are observed in sEMG amplitude, frequency and fractal dimension. It is also observed that at different levels of muscle contractions there is similarity in the relationships with Root Mean Square, Median Frequency, and Fractal Dimension of the recorded and simulated sEMG signals.

Convolutive blind source separation of surface EMG measurements of the respiratory muscles

Electromyography (EMG) has long been used for the assessment of muscle function and activity and has recently been applied to the control of medical ventilation. For this application, the EMG signal is usually recorded invasively by means of electrodes on a nasogastric tube which is placed inside the esophagus in order to minimize noise and crosstalk from other muscles. Replacing these invasive measurements with an EMG signal obtained non-invasively on the body surface is difficult and requires techniques for signal separation in order to reconstruct the contributions of the individual respiratory muscles. In the case of muscles with small cross-sectional areas, or with muscles at large distances from the recording site, solutions to this problem have been proposed previously. The respiratory muscles, however, are large and distributed widely over the upper body volume. In this article, we describe an algorithm for convolutive blind source separation (BSS) that performs well even for large, distributed muscles such as the respiratory muscles, while using only a small number of electrodes. The algorithm is derived as a special case of the TRINICON general framework for BSS. To provide evidence that it shows potential for separating inspiratory, expiratory, and cardiac activities in practical applications, a joint numerical simulation of EMG and ECG activities was performed, and separation success was evaluated in a variety of noise settings. The results are promising.

Generation of synthetic surface electromyography signals under fatigue conditions for varying force inputs using feedback control algorithm

Surface electromyography is a non-invasive technique used for recording the electrical activity of neuromuscular systems. These signals are random, complex and multi-component. There are several techniques to extract information about the force exerted by muscles during any activity. This work attempts to generate surface electromyography signals for various magnitudes of force under isometric non-fatigue and fatigue conditions using a feedback model. The model is based on existing current distribution, volume conductor relations, the feedback control algorithm for rate coding and generation of firing pattern. The result shows that synthetic surface electromyography signals are highly complex in both non-fatigue and fatigue conditions. Furthermore, surface electromyography signals have higher amplitude and lower frequency under fatigue condition. This model can be used to study the influence of various signal parameters under fatigue and non-fatigue conditions.

Modifying motor unit territory placement in the Fuglevand model

The Fuglevand model is often used to address challenging questions in neurophysiology; however, there are elements of the neuromuscular system unaccounted for in the model. For instance, in some muscles, slow and fast motor units (MUs) tend to reside deep and superficially in the muscle, respectively, necessarily altering the development of surface electromyogram (EMG) power during activation. Thus, the objective of this study was to replace the randomized MU territory (MUT) placement algorithm in the Fuglevand model with an optimized method capable of reflecting these observations. To accomplish this, a weighting term was added to a previously developed optimization algorithm to encourage regionalized MUT placement. The weighting term consequently produced significantly different muscle fibre type content in the deep and superficial portions of the muscle. The relation between simulated EMG and muscle force was found to be significantly affected by regionalization. These changes were specifically a function of EMG power, as force was unaffected by regionalization. These findings suggest that parameterizing MUT regionalization will allow the model to produce a larger variety of EMG-force relations, as is observed physiologically, and could potentially simulate the loss of specific MU types as observed in ageing and clinical populations.

A new approach for multi-channel surface EMG signal simulation

Simulation models are necessary for testing the performance of newly developed approaches before they can be applied to interpreting experimental data, especially when biomedical signals such as surface electromyogram (SEMG) signals are involved. A new and easily implementable surface EMG simulation model was developed in this study to simulate multi-channel SEMG signals. A single fiber action potential (SFAP) is represented by the sum of three Gaussian functions. SFAP waveforms can be modified by adjusting the amplitude and bandwidth of the Gaussian functions. SEMG signals were successfully simulated at different detected locations. Effects of the fiber depth, electrode position and conduction velocity of SFAP on motor unit action potential (MUAP) were illustrated. Results demonstrate that the easily implementable SEMG simulation approach developed in this study can be used to effectively simulate SEMG signals.

Detection of Multiple Innervation Zones from Multi-Channel Surface EMG Recordings with Low Signal-to-Noise Ratio Using Graph-Cut Segmentation

Knowledge of the location of muscle Innervation Zones (IZs) is important in many applications, e.g. for minimizing the quantity of injected botulinum toxin for the treatment of spasticity or for deciding on the type of episiotomy during child delivery. Surface EMG (sEMG) can be noninvasively recorded to assess physiological and morphological characteristics of contracting muscles. However, it is not often possible to record signals of high quality. Moreover, muscles could have multiple IZs, which should all be identified. We designed a fully-automatic algorithm based on the enhanced image Graph-Cut segmentation and morphological image processing methods to identify up to five IZs in 60-ms intervals of very-low to moderate quality sEMG signal detected with multi-channel electrodes (20 bipolar channels with Inter Electrode Distance (IED) of 5 mm). An anisotropic multilayered cylinder model was used to simulate 750 sEMG signals with signal-to-noise ratio ranging from -5 to 15 dB (using Gaussian noise) and in each 60-ms signal frame, 1 to 5 IZs were included. The micro- and macro- averaged performance indices were then reported for the proposed IZ detection algorithm. In the micro-averaging procedure, the number of True Positives, False Positives and False Negatives in each frame were summed up to generate cumulative measures. In the macro-averaging, on the other hand, precision and recall were calculated for each frame and their averages are used to determine F1-score. Overall, the micro (macro)-averaged sensitivity, precision and F1-score of the algorithm for IZ channel identification were 82.7% (87.5%), 92.9% (94.0%) and 87.5% (90.6%), respectively. For the correctly identified IZ locations, the average bias error was of 0.02±0.10 IED ratio. Also, the average absolute conduction velocity estimation error was 0.41±0.40 m/s for such frames. The sensitivity analysis including increasing IED and reducing interpolation coefficient for time samples was performed. Meanwhile, the effect of adding power-line interference and using other image interpolation methods on the deterioration of the performance of the proposed algorithm was investigated. The average running time of the proposed algorithm on each 60-ms sEMG frame was 25.5±8.9 (s) on an Intel dual-core 1.83 GHz CPU with 2 GB of RAM. The proposed algorithm correctly and precisely identified multiple IZs in each signal epoch in a wide range of signal quality and is thus a promising new offline tool for electrophysiological studies.

Convolutive blind source separation on surface EMG signals for respiratory diagnostics and medical ventilation control

The electromyogram (EMG) is an important tool for assessing the activity of a muscle and thus also a valuable measure for the diagnosis and control of respiratory support. In this article we propose convolutive blind source separation (BSS) as an effective tool to pre-process surface electromyogram (sEMG) data of the human respiratory muscles. Specifically, the problem of discriminating between inspiratory, expiratory and cardiac muscle activity is addressed, which currently poses a major obstacle for the clinical use of sEMG for adaptive ventilation control. It is shown that using the investigated broadband algorithm, a clear separation of these components can be achieved. The algorithm is based on a generic framework for BSS that utilizes multiple statistical signal characteristics. Apart from a four-channel FIR structure, there are no further restrictive assumptions on the demixing system.

Age related neuromuscular changes in sEMG of m. Tibialis Anterior using higher order statistics (Gaussianity & linearity test)

Age-associated changes in the surface electromyogram (sEMG) of Tibialis Anterior (TA) muscle can be attributable to neuromuscular alterations that precede strength loss. We have used our sEMG model of the Tibialis Anterior to interpret the age-related changes and compared with the experimental sEMG. Eighteen young (20-30 years) and 18 older (60-85 years) performed isometric dorsiflexion at 6 different percentage levels of maximum voluntary contractions (MVC), and their sEMG from the TA muscle was recorded. Six different age-related changes in the neuromuscular system were simulated using the sEMG model at the same MVCs as the experiment. The maximal power of the spectrum, Gaussianity and Linearity Test Statistics were computed from the simulated and experimental sEMG. A correlation analysis at α=0.05 was performed between the simulated and experimental age-related change in the sEMG features. The results show the loss in motor units was distinguished by the Gaussianity and Linearity test statistics; while the maximal power of the PSD distinguished between the muscular factors. The simulated condition of 40% loss of motor units with halved the number of fast fibers best correlated with the age-related change observed in the experimental sEMG higher order statistical features. The simulated aging condition found by this study corresponds with the moderate motor unit remodelling and negligible strength loss reported in literature for the cohorts aged 60-70 years.