High-density surface electromyography signals during isometric contractions of elbow muscles of healthy humans

Improving localization and measurements of M-waves using high-density surface electromyography

Surface electromyography (sEMG) is useful for studying muscle function and controlling prosthetics, but cross talk from nearby muscles often limits its effectiveness. High-density surface EMG (HD-sEMG) improves spatial resolution, allowing for the isolation of M-waves in the densely packed forearm muscles. This study assessed HD-sEMG for localizing M-waves and evaluated the impact of spatial filters on cross talk reduction. We administered peripheral nerve stimulation to activate forearm muscles in five participants. We analyzed cross talk by correlating the shape of M-waves between electrodes and used ultrasound to confirm muscle identity and location. At low-stimulation intensities, we successfully isolated M-waves with minimal cross talk without spatial filtering. Higher recruitment levels produced significant cross talk, which was reduced by applying bipolar or tripolar spatial filters. M-waves from the monopolar HD-sEMG montage showed high correlations between electrodes (r = 0.97 transversely; r = 0.95 longitudinally), while bipolar and tripolar montages showed lower correlations (bipolar: r = 0.41 transversely; r = 0.19 longitudinally; tripolar: r = 0.17 transversely; r = 0.01 longitudinally). The tripolar filter significantly reduced cross talk (51.10% amplitude decay one electrode away) compared with no filter (10.32% amplitude decay one electrode away), effectively reducing cross talk to negligible levels at distances ≥2.55 cm. Ultrasound was crucial for distinguishing true activation from artifacts caused by converging signals along muscle boundaries. Spatially filtered HD-sEMG accurately detects and isolates M-waves in the forearm, and ultrasound imaging is useful for verifying the location and identity of the muscles underlying the HD-sEMG grids.NEW & NOTEWORTHY This study introduces an innovative approach to enhancing evoked potential measurements using high-density surface electromyography (HD-sEMG). The precision and localization of evoked potentials are significantly improved by spatial filters and ultrasound imaging, offering a novel method for better assessing motor pathway integrity. These advancements could lead to more accurate tools for detecting and treating neurological deficits, making it a significant contribution to neurophysiological research.

Non-Invasive and Quantitative Evaluation for Disuse Muscle Atrophy Caused by Immobilization After Limb Fracture Based on Surface Electromyography Analysis

BACKGROUND

The clinical evaluation for disuse muscle atrophy usually depends on qualitative rating indicators with subjective judgments of doctors and some invasive measurement methods such as needle electromyography. Surface electromyography, as a non-invasive method, has been widely used in the detection of muscular and neurological diseases in recent years. In this paper, we explore how to evaluate disuse muscle atrophy based on surface electromyography; Methods: Firstly, we conducted rat experiments using hind-limb suspension to create a model of disuse muscle atrophy. Five groups of rats were suspended for 0, 3, 7, 14, and 21 days, respectively. We induced leg electromyography of rats through electrical stimulation and used fluorescence staining to obtain the fiber-type composition of rats' leg muscles. We obtained the best-fitting frequency bands of power spectrum density of surface electromyography for type I and type II fibers in rats' leg muscles by changing the frequency band boundaries. Secondly, we conducted tests on the human body and collected the electromyography of the atrophied muscles of the subjects over a period of 21 days. The changes in muscle fiber composition were evaluated using the frequency bands of power spectrum density obtained from rat experiments. The method was to evaluate the changes in type I fibers by the changes in the area of the best-fitting frequency band of type I fibers and to evaluate the changes in type II fibers by the changes in the area of the best-fitting frequency band of type II fibers.

RESULTS

The results of rat experiments showed that type I fibers best fit the frequency band of 20-330 Hz and type II fibers best fit the frequency band of 176-500 Hz. The results of human testing showed that the atrophy of the two types of fibers was consistent with the changes in the areas of the corresponding best-fitting frequency bands.

CONCLUSIONS

The test results demonstrate the feasibility of using surface electromyography to evaluate muscle fiber-type composition and subsequently assess muscle atrophy. Further research may contribute to the diagnosis and treatment of disuse muscle atrophy.

Hand Gesture Recognition Based on High-Density Myoelectricity in Forearm Flexors in Humans

Electromyography-based gesture recognition has become a challenging problem in the decoding of fine hand movements. Recent research has focused on improving the accuracy of gesture recognition by increasing the complexity of network models. However, training a complex model necessitates a significant amount of data, thereby escalating both user burden and computational costs. Moreover, owing to the considerable variability of surface electromyography (sEMG) signals across different users, conventional machine learning approaches reliant on a single feature fail to meet the demand for precise gesture recognition tailored to individual users. Therefore, to solve the problems of large computational cost and poor cross-user pattern recognition performance, we propose a feature selection method that combines mutual information, principal component analysis and the Pearson correlation coefficient (MPP). This method can filter out the optimal subset of features that match a specific user while combining with an SVM classifier to accurately and efficiently recognize the user's gesture movements. To validate the effectiveness of the above method, we designed an experiment including five gesture actions. The experimental results show that compared to the classification accuracy obtained using a single feature, we achieved an improvement of about 5% with the optimally selected feature as the input to any of the classifiers. This study provides an effective guarantee for user-specific fine hand movement decoding based on sEMG signals.

The Functional and Anatomical Impacts of Healthy Muscle Ageing

Even "healthy" muscle ageing is often associated with substantial changes in muscle form and function and can lead to increased injury risks and significant negative impacts on quality of life. However, the impacts of healthy muscle ageing on the fibre architecture and microstructure of different muscles and muscle groups throughout the lower limb, and how these are related to their functional capabilities, are not fully understood. Here, a previously established framework of magnetic resonance and diffusion tensor imaging was used to measure the muscle volumes, intramuscular fat, fibre lengths and physiological cross-sectional areas of 12 lower limb muscles in a cohort of healthily aged individuals, which were compared to the same data from a young population. Maximum muscle forces were also measured from an isokinetic dynamometer. The more substantial interpopulation differences in architecture and functional performance were located within the knee extensor muscles, while the aged muscles were also more heterogeneous in muscle fibre type and atrophy. The relationships between architecture and muscle strength were also more significant in the knee extensors compared to other functional groups. These data highlight the importance of the knee extensors as a potential focus for interventions to negate the impacts of muscle ageing.

Transformer-based hand gesture recognition from instantaneous to fused neural decomposition of high-density EMG signals

Designing efficient and labor-saving prosthetic hands requires powerful hand gesture recognition algorithms that can achieve high accuracy with limited complexity and latency. In this context, the paper proposes a Compact Transformer-based Hand Gesture Recognition framework referred to as [Formula: see text], which employs a vision transformer network to conduct hand gesture recognition using high-density surface EMG (HD-sEMG) signals. Taking advantage of the attention mechanism, which is incorporated into the transformer architectures, our proposed [Formula: see text] framework overcomes major constraints associated with most of the existing deep learning models such as model complexity; requiring feature engineering; inability to consider both temporal and spatial information of HD-sEMG signals, and requiring a large number of training samples. The attention mechanism in the proposed model identifies similarities among different data segments with a greater capacity for parallel computations and addresses the memory limitation problems while dealing with inputs of large sequence lengths. [Formula: see text] can be trained from scratch without any need for transfer learning and can simultaneously extract both temporal and spatial features of HD-sEMG data. Additionally, the [Formula: see text] framework can perform instantaneous recognition using sEMG image spatially composed from HD-sEMG signals. A variant of the [Formula: see text] is also designed to incorporate microscopic neural drive information in the form of Motor Unit Spike Trains (MUSTs) extracted from HD-sEMG signals using Blind Source Separation (BSS). This variant is combined with its baseline version via a hybrid architecture to evaluate potentials of fusing macroscopic and microscopic neural drive information. The utilized HD-sEMG dataset involves 128 electrodes that collect the signals related to 65 isometric hand gestures of 20 subjects. The proposed [Formula: see text] framework is applied to 31.25, 62.5, 125, 250 ms window sizes of the above-mentioned dataset utilizing 32, 64, 128 electrode channels. Our results are obtained via 5-fold cross-validation by first applying the proposed framework on the dataset of each subject separately and then, averaging the accuracies among all the subjects. The average accuracy over all the participants using 32 electrodes and a window size of 31.25 ms is 86.23%, which gradually increases till reaching 91.98% for 128 electrodes and a window size of 250 ms. The [Formula: see text] achieves accuracy of 89.13% for instantaneous recognition based on a single frame of HD-sEMG image. The proposed model is statistically compared with a 3D Convolutional Neural Network (CNN) and two different variants of Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) models. The accuracy results for each of the above-mentioned models are paired with their precision, recall, F1 score, required memory, and train/test times. The results corroborate effectiveness of the proposed [Formula: see text] framework compared to its counterparts.

Detection and Reconstruction of Poor-Quality Channels in High-Density EMG Array Measurements

High-density electromyography (HD-EMG) arrays allow for the study of muscle activity in both time and space by recording electrical potentials produced by muscle contractions. HD-EMG array measurements are susceptible to noise and artifacts and frequently contain some poor-quality channels. This paper proposes an interpolation-based method for the detection and reconstruction of poor-quality channels in HD-EMG arrays. The proposed detection method identified artificially contaminated channels of HD-EMG for signal-to-noise ratio (SNR) levels 0 dB and lower with ≥99.9% precision and ≥97.6% recall. The interpolation-based detection method had the best overall performance compared with two other rule-based methods that used the root mean square (RMS) and normalized mutual information (NMI) to detect poor-quality channels in HD-EMG data. Unlike other detection methods, the interpolation-based method evaluated channel quality in a localized context in the HD-EMG array. For a single poor-quality channel with an SNR of 0 dB, the F1 scores for the interpolation-based, RMS, and NMI methods were 99.1%, 39.7%, and 75.9%, respectively. The interpolation-based method was also the most effective detection method for identifying poor channels in samples of real HD-EMG data. F1 scores for the detection of poor-quality channels in real data for the interpolation-based, RMS, and NMI methods were 96.4%, 64.5%, and 50.0%, respectively. Following the detection of poor-quality channels, 2D spline interpolation was used to successfully reconstruct these channels. Reconstruction of known target channels had a percent residual difference (PRD) of 15.5 ± 12.1%. The proposed interpolation-based method is an effective approach for the detection and reconstruction of poor-quality channels in HD-EMG.

From fibre to function: are we accurately representing muscle architecture and performance?

The size and arrangement of fibres play a determinate role in the kinetic and energetic performance of muscles. Extrapolations between fibre architecture and performance underpin our understanding of how muscles function and how they are adapted to power specific motions within and across species. Here we provide a synopsis of how this 'fibre to function' paradigm has been applied to understand muscle design, performance and adaptation in animals. Our review highlights the widespread application of the fibre to function paradigm across a diverse breadth of biological disciplines but also reveals a potential and highly prevalent limitation running through past studies. Specifically, we find that quantification of muscle architectural properties is almost universally based on an extremely small number of fibre measurements. Despite the volume of research into muscle properties, across a diverse breadth of research disciplines, the fundamental assumption that a small proportion of fibre measurements can accurately represent the architectural properties of a muscle has never been quantitatively tested. Subsequently, we use a combination of medical imaging, statistical analysis, and physics-based computer simulation to address this issue for the first time. By combining diffusion tensor imaging (DTI) and deterministic fibre tractography we generated a large number of fibre measurements (>3000) rapidly for individual human lower limb muscles. Through statistical subsampling simulations of these measurements, we demonstrate that analysing a small number of fibres (n < 25) typically used in previous studies may lead to extremely large errors in the characterisation of overall muscle architectural properties such as mean fibre length and physiological cross-sectional area. Through dynamic musculoskeletal simulations of human walking and jumping, we demonstrate that recovered errors in fibre architecture characterisation have significant implications for quantitative predictions of in-vivo dynamics and muscle fibre function within a species. Furthermore, by applying data-subsampling simulations to comparisons of muscle function in humans and chimpanzees, we demonstrate that error magnitudes significantly impact both qualitative and quantitative assessment of muscle specialisation, potentially generating highly erroneous conclusions about the absolute and relative adaption of muscles across species and evolutionary transitions. Our findings have profound implications for how a broad diversity of research fields quantify muscle architecture and interpret muscle function.

Non-invasive assessment of swallowing using flexible high-density electromyography arrays

Swallowing is a vital function that serves to safely transport food and fluid to the stomach, while simultaneously protecting our airways. Evaluation of swallowing is important for the diagnosis and rehabilitation of individuals with dysphagia, a disorder of swallowing. Flexible high-density surface electromyography (HD sEMG) arrays were designed and fabricated to span the floor of mouth and neck muscles. These arrays were applied on 6 healthy participants over duplicate recording sessions. During each recording session, participants performed three different swallowing motor tasks. The HD sEMG signals were filtered and tasks extracted. For each task, the RMS amplitude was computed, visualized, and compared. Dynamic motor coordination was evident in the filtered signals traces, with different electrode locations showing unique temporal activations. The 2D topographical maps allowed the location of different RMS intensities to be visualized, revealing qualitatively similar patterns across participants and tasks. These motor task trends were also seen within RMS quantifications. The RMS metric across all participants identified significant differences between non-effortful 3 ml and effortful 3 ml swallow tasks ( p=0.006) and there was a minimal variation of 3.1±1.9 μV RMS for repeated recording sessions by each participant. The HD-sEMG array successfully recorded differences in muscle activations during swallowing and was able to discern between two different motor tasks. The arrays offers a spatially detailed non-invasive assessment of the neuromuscular performance of swallowing. Clinical Relevance- The utility of HD-sEMG arrays for evaluation of the muscles involved in swallowing could enable diagnosis and rehabilitation of individuals with dysphagia.

Materials for Implantable Surface Electrode Arrays: Current Status and Future Directions

Surface electrode arrays are mainly fabricated from rigid or elastic materials, and precisely manipulated ductile metal films, which offer limited stretchability. However, the living tissues to which they are applied are nonlinear viscoelastic materials, which can undergo significant mechanical deformation in dynamic biological environments. Further, the same arrays and compositions are often repurposed for vastly different tissues rather than optimizing the materials and mechanical properties of the implant for the target application. By first characterizing the desired biological environment, and then designing a technology for a particular organ, surface electrode arrays may be more conformable, and offer better interfaces to tissues while causing less damage. Here, the various materials used in each component of a surface electrode array are first reviewed, and then electrically active implants in three specific biological systems, the nervous system, the muscular system, and skin, are described. Finally, the fabrication of next-generation surface arrays that overcome current limitations is discussed.

A Preliminary Study on the Use of HD-sEMG for the Functional Imaging of Equine Superficial Muscle Activation during Dynamic Mobilization Exercises

Superficial skeletal muscle activation is associated with an electric activity. Bidimensional High-Density Surface Electromyography (HD-sEMG) is a non-invasive technique that uses a grid of equally spaced electrodes applied on the skin surface to detect and portray superficial skeletal muscle activation. The goal of the study was to evaluate the feasibility of HD-sEMG to detect electrical activation of skeletal muscle and its application during rehabilitation exercises in horses. To fulfil this aim, activation of the superficial descending pectoral and external abdominal oblique core muscles were measured using HD-sEMG technology during dynamic mobilization exercises to induce lateral bending and flexion/extension tasks of the trunk. Masseter muscle was instrumented during mastication as a control condition. A 64 surface EMG channel wireless system was used with a single 64 electrode grid or a pair of 32 electrode grids. HD-sEMG provided unique information on the muscular activation onset, duration, and offset, along each motor task, and permitting inferences about the motor control strategy actuated by the central nervous system. Signals were further processed to obtain firing frequencies of few motor-neurons. Estimation of electromyographic amplitude and spectral parameters allowed detecting the onset of muscular fatigue during the motor tasks performed. HD-sEMG allows the assessment of muscular activation in horses performing specific motor tasks, supporting its future application in clinical and research settings.

High-Density Electromyography Provides Improved Understanding of Muscle Function for Those With Amputation

Transtibial amputation can significantly impact an individual's quality of life including the completion of activities of daily living. Those with lower limb amputations can harness the electrical activity from their amputated limb muscles for myoelectric control of a powered prosthesis. While these devices use residual muscles from transtibial-amputated limb as an input to the controller, there is little research characterizing the changes in surface electromyography (sEMG) signal generated by the upper leg muscles. Traditional surface EMG is limited in the number of electrode sites while high-density surface EMG (HDsEMG) uses multiple electrode sites to gather more information from the muscle. This technique is promising for not only the development of myoelectric-controlled prostheses but also advancing our knowledge of muscle behavior with clinical populations, including post-amputation. The HDsEMG signal can be used to develop spatial activation maps and features of these maps can be used to gain valuable insight into muscle behavior. Spatial features of HDsEMG can provide information regarding muscle activation, muscle fiber heterogeneity, and changes in muscle distribution and can be used to estimate properties of both the amputated limb and intact limb. While there are a few studies that have examined HDsEMG in amputated lower limbs they have been limited to movements such as gait. The purpose of this study was to examine the quadriceps muscle during a slow, moderate and fast isokinetic knee extensions from a control group as well as a clinical patient with a transtibial amputation. HDsEMG was collected from the quadriceps of the dominant leg of 14 young, healthy males (mean age = 25.5 ± 7 years old). Signals were collected from both the intact and amputated limb muscle of a 23 year old clinical participant to examine differences between the affected and unaffected leg. It was found that there were differences between the intact and amputated limb limb of the clinical participant with respect to muscle activation and muscle heterogeneity. While this study was limited to one clinical participant, it is important to note the differences in muscle behavior between the intact and amputated limb limb. Understanding these differences will help to improve training protocols for those with amputation.