EMG-Torque Relation in Chronic Stroke: A Novel EMG Complexity Representation With a Linear Electrode Array

Pedaling Asymmetry Reflected by Bilateral EMG Complexity in Chronic Stroke

This study examines pedaling asymmetry using the electromyogram (EMG) complexity of six bilateral lower limb muscles for chronic stroke survivors. Fifteen unilateral chronic stroke and twelve healthy participants joined passive and volitional recumbent pedaling tasks using a self-modified stationary bike with a constant speed of 25 revolutions per minute. The fuzzy approximate entropy (fApEn) was adopted in EMG complexity estimation. EMG complexity values of stroke participants during pedaling were smaller than those of healthy participants (p = 0.002). For chronic stroke participants, the complexity of paretic limbs was smaller than that of non-paretic limbs during the passive pedaling task (p = 0.005). Additionally, there was a significant correlation between clinical scores and the paretic EMG complexity during passive pedaling (p = 0.022, p = 0.028), indicating that the paretic EMG complexity during passive movement might serve as an indicator of stroke motor function status. This study suggests that EMG complexity is an appropriate quantitative tool for measuring neuromuscular characteristics in lower limb dynamic movement tasks for chronic stroke survivors.

Post-stroke hand gesture recognition via one-shot transfer learning using prototypical networks

BACKGROUND

In-home rehabilitation systems are a promising, potential alternative to conventional therapy for stroke survivors. Unfortunately, physiological differences between participants and sensor displacement in wearable sensors pose a significant challenge to classifier performance, particularly for people with stroke who may encounter difficulties repeatedly performing trials. This makes it challenging to create reliable in-home rehabilitation systems that can accurately classify gestures.

METHODS

Twenty individuals who suffered a stroke performed seven different gestures (mass flexion, mass extension, wrist volar flexion, wrist dorsiflexion, forearm pronation, forearm supination, and rest) related to activities of daily living. They performed these gestures while wearing EMG sensors on the forearm, as well as FMG sensors and an IMU on the wrist. We developed a model based on prototypical networks for one-shot transfer learning, K-Best feature selection, and increased window size to improve model accuracy. Our model was evaluated against conventional transfer learning with neural networks, as well as subject-dependent and subject-independent classifiers: neural networks, LGBM, LDA, and SVM.

RESULTS

Our proposed model achieved 82.2% hand-gesture classification accuracy, which was better (P<0.05) than one-shot transfer learning with neural networks (63.17%), neural networks (59.72%), LGBM (65.09%), LDA (63.35%), and SVM (54.5%). In addition, our model performed similarly to subject-dependent classifiers, slightly lower than SVM (83.84%) but higher than neural networks (81.62%), LGBM (80.79%), and LDA (74.89%). Using K-Best features improved the accuracy in 3 of the 6 classifiers used for evaluation, while not affecting the accuracy in the other classifiers. Increasing the window size improved the accuracy of all the classifiers by an average of 4.28%.

CONCLUSION

Our proposed model showed significant improvements in hand-gesture recognition accuracy in individuals who have had a stroke as compared with conventional transfer learning, neural networks and traditional machine learning approaches. In addition, K-Best feature selection and increased window size can further improve the accuracy. This approach could help to alleviate the impact of physiological differences and create a subject-independent model for stroke survivors that improves the classification accuracy of wearable sensors.

TRIAL REGISTRATION NUMBER

The study was registered in Chinese Clinical Trial Registry with registration number CHiCTR1800017568 in 2018/08/04.

Clustering index analysis on EMG-Torque relation-based representation of complex neuromuscular changes after spinal cord injury

Spinal cord injury (SCI) resulting in complex neuromuscular pathology is not sufficiently well understood. To better quantify neuromuscular changes after SCI, this study uses a clustering index (CI) method for surface electromyography (sEMG) clustering representation to investigate the relation between sEMG and torque in SCI survivors. The sEMG signals were recorded from 13 subjects with SCI and 13 gender-age matched able-bodied subjects during isometric contraction of the biceps brachii muscle at different torque levels using a linear electrode array. Two torque representations, maximum voluntary contraction (MVC%) and absolute torque, were used. CI values were calculated for sEMG. Regression analyses were performed on CI values and torque levels of elbow flexion, revealing a strong linear relationship. The slopes of regressions between SCI survivors and control subjects were compared. The findings indicated that the range of distribution of CI values and slopes was greater in subjects with SCI than in control subjects (p < 0.05). The increase or decrease in slope was also observed at the individual level. This suggests that the CI and its sEMG clustering-torque relation may serve as valuable quantitative indicators for determining neuromuscular lesions after SCI, contributing to the development of effective rehabilitation strategies for improving motor performance.

Biomechanical Assessment Methods Used in Chronic Stroke: A Scoping Review of Non-Linear Approaches

Non-linear and dynamic systems analysis of human movement has recently become increasingly widespread with the intention of better reflecting how complexity affects the adaptability of motor systems, especially after a stroke. The main objective of this scoping review was to summarize the non-linear measures used in the analysis of kinetic, kinematic, and EMG data of human movement after stroke. PRISMA-ScR guidelines were followed, establishing the eligibility criteria, the population, the concept, and the contextual framework. The examined studies were published between 1 January 2013 and 12 April 2023, in English or Portuguese, and were indexed in the databases selected for this research: PubMed®, Web of Science®, Institute of Electrical and Electronics Engineers®, Science Direct® and Google Scholar®. In total, 14 of the 763 articles met the inclusion criteria. The non-linear measures identified included entropy (n = 11), fractal analysis (n = 1), the short-term local divergence exponent (n = 1), the maximum Floquet multiplier (n = 1), and the Lyapunov exponent (n = 1). These studies focused on different motor tasks: reaching to grasp (n = 2), reaching to point (n = 1), arm tracking (n = 2), elbow flexion (n = 5), elbow extension (n = 1), wrist and finger extension upward (lifting) (n = 1), knee extension (n = 1), and walking (n = 4). When studying the complexity of human movement in chronic post-stroke adults, entropy measures, particularly sample entropy, were preferred. Kinematic assessment was mainly performed using motion capture systems, with a focus on joint angles of the upper limbs.

Online prediction of sustained muscle force from individual motor unit activities using adaptive surface EMG decomposition

Decoding movement intentions from motor unit (MU) activities to represent neural drive information plays a central role in establishing neural interfaces, but there remains a great challenge for obtaining precise MU activities during sustained muscle contractions. In this paper, we presented an online muscle force prediction method driven by individual MU activities that were decomposed from prolonged surface electromyogram (SEMG) signals in real time. In the training stage of the proposed method, a set of separation vectors was initialized for decomposing MU activities. After transferring each decomposed MU activity into a twitch force train according to its action potential waveform, a neural network was designed and trained for predicting muscle force. In the subsequent online stage, a practical double-thread-parallel algorithm was developed. One frontend thread predicted the muscle force in real time utilizing the trained network and the other backend thread simultaneously updated the separation vectors. To assess the performance of the proposed method, SEMG signals were recorded from the abductor pollicis brevis muscles of eight subjects and the contraction force was simultaneously collected. With the update procedure in the backend thread, the force prediction performance of the proposed method was significantly improved in terms of lower root mean square deviation (RMSD) of around 10% and higher fitness (R2) of around 0.90, outperforming two conventional methods. This study provides a promising technique for real-time myoelectric applications in movement control and health.

Interpreting Stroke-Impaired Electromyography Patterns through Explainable Artificial Intelligence

Electromyography (EMG) proves invaluable myoelectric manifestation in identifying neuromuscular alterations resulting from ischemic strokes, serving as a potential marker for diagnostics of gait impairments caused by ischemia. This study aims to develop an interpretable machine learning (ML) framework capable of distinguishing between the myoelectric patterns of stroke patients and those of healthy individuals through Explainable Artificial Intelligence (XAI) techniques. The research included 48 stroke patients (average age 70.6 years, 65% male) undergoing treatment at a rehabilitation center, alongside 75 healthy adults (average age 76.3 years, 32% male) as the control group. EMG signals were recorded from wearable devices positioned on the bicep femoris and lateral gastrocnemius muscles of both lower limbs during indoor ground walking in a gait laboratory. Boosting ML techniques were deployed to identify stroke-related gait impairments using EMG gait features. Furthermore, we employed XAI techniques, such as Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and Anchors to interpret the role of EMG variables in the stroke-prediction models. Among the ML models assessed, the GBoost model demonstrated the highest classification performance (AUROC: 0.94) during cross-validation with the training dataset, and it also overperformed (AUROC: 0.92, accuracy: 85.26%) when evaluated using the testing EMG dataset. Through SHAP and LIME analyses, the study identified that EMG spectral features contributing to distinguishing the stroke group from the control group were associated with the right bicep femoris and lateral gastrocnemius muscles. This interpretable EMG-based stroke prediction model holds promise as an objective tool for predicting post-stroke gait impairments. Its potential application could greatly assist in managing post-stroke rehabilitation by providing reliable EMG biomarkers and address potential gait impairment in individuals recovering from ischemic stroke.

Compound muscle action potential (CMAP) scan examination of paretic and contralateral muscles reveals motor unit alterations after stroke

This study presents a novel compound muscle action potential (CMAP) examination of motor unit changes in paretic muscle post stroke. CMAP scan of the first dorsal interosseous (FDI) muscle was performed bilaterally in 16 chronic stroke subjects. Various parameters were derived from the CMAP scan to examine paretic muscle changes, including CMAP amplitude, D50, step index (STEPIX) and amplitude index (AMPIX). A significant decrease in CMAP amplitude and STEPIX was observed in paretic muscles compared with contralateral muscles (CMAP amplitude: paretic (9.0±0.5) mV, contralateral (11.3±0.9) mV, P=0.024; STEPIX: paretic 101.2±7.6, contralateral 121.9±6.5, P=0.020). No significant difference in D50 and AMPIX was observed between the paretic and contralateral sides (P>0.05). The findings revealed complex paretic muscle changes including motor unit degeneration, muscle fiber denervation, reinnervation and atrophy, providing useful insights to help understand neuromuscular mechanisms associated with weakness and other functional deterioration post stroke. The CMAP scan experimental protocols and the applied processing methods are noninvasive, convenient, and automated, offering practical benefits for clinical application.

Electromyography-Force Relation and Muscle Fiber Conduction Velocity Affected by Spinal Cord Injury

A surface electromyography (EMG) analysis was performed in this study to examine central neural and peripheral muscle changes after a spinal cord injury (SCI). A linear electrode array was used to record surface EMG signals from the biceps brachii (BB) in 15 SCI subjects and 14 matched healthy control subjects as they performed elbow flexor isometric contractions from 10% to 80% maximum voluntary contraction. Muscle fiber conduction velocity (MFCV) and BB EMG-force relation were examined. MFCV was found to be significantly slower in the SCI group than the control group, evident at all force levels. The BB EMG-force relation was well fit by quadratic functions in both groups. All healthy control EMG-force relations were best fit with positive quadratic coefficients. In contrast, the EMG-force relation in eight SCI subjects was best fit with negative quadratic coefficients, suggesting impaired EMG modulation at high forces. The alterations in MFCV and EMG-force relation after SCI suggest complex neuromuscular changes after SCI, including alterations in central neural drive and muscle properties.

Relationship between EMG features and force in orbicularis oris muscle

BACKGROUND

Lip incompetence resulting from mouth breathing is a common clinical manifestation, while there are no definite indicators of amplitude and intensity of muscle functional training in clinical practice, which leads to unsatisfactory training results.

OBJECTIVE

The aim was to quantify the relationship between electromyography (EMG) and force in orbicularis oris muscle, so that the indicators of muscle functional training can be evaluated using EMG signals, so as to improve the training effects.

METHODS

The EMG and the force signals of orbicularis oris muscle from 0% to 100% MVC within 5 s in twelve healthy subjects (six males and six females; age, 25 ± 2 years; mass, 60 ± 15 kg) were recorded simultaneously for three trials. Four EMG features consisting of RMS, WAMP, SampEn and FuzzyEn were analyzed. The regression analyses were performed using first-order and third-order polynomial model.

RESULTS

There were high correlations between the four EMG features and muscle force with the two models. The third-order model yielded a higher coefficient of determination (R2) than the linear model (p< 0.001) and the result of FuzzyEn (R2: 0.884 ± 0.059) was the highest in the four features.

CONCLUSION

The third-order model with FuzzyEn of EMG signals may be used to guide the muscle functional training.

Decoding Muscle Force From Motor Unit Firings Using Encoder-Decoder Networks

Appropriate interpretation of motor unit (MU) activities after surface EMG (sEMG) decomposition is a key factor to decode motor intentions in a noninvasive and physiologically meaningful way. However, there are great challenges due to the difficulty in cross-trial MU tracking and unavoidable loss of partial MU information resulting from incomplete decomposition. In light of these issues, this study presents a novel framework for interpreting MU activities and applies it to decode muscle force. The resulting MUs were clustered and classified into different categories by characterizing their spatially distributed firing waveforms. The process served as a general MU tracking method. On this basis, after transferring the MU firing trains to twitch force trains by a twitch force model, a deep network was designed to predict the normalized force. In addition, MU category distribution was examined to calibrate the actual force level, while functions of some unavailable MUs were compensated. To investigate the effectiveness of this framework, high-density sEMG signals were recorded using an 8×8 electrode array from the abductor pollicis brevis muscles of eight subjects, while thumb abduction force was measured. The proposed method outperformed three common methods ( ) yielding the lowest root mean square deviation of 6.68% ± 1.29% and the highest fitness ( R² ) of 0.94 ± 0.04 between the predicted force and the actual force. This study offers a valuable, computational solution for interpreting individual MU activities, and its effectiveness was confirmed in muscle force estimation.

Muscle Force Estimation Based on Neural Drive Information From Individual Motor Units

Estimation of muscle contraction force based on the macroscopic feature of surface electromyography (SEMG) has been widely reported, but the use of microscopic neural drive information has not been thoroughly investigated. In this study, a novel method is proposed to process individual motor unit (MU) activities (firing sequences and action potential waveforms) derived from the decomposition of high density SEMG (HD-SEMG), and it is applied to muscle force estimation. In the proposed method, a supervised machine learning approach was conducted to determine the twitch force of each MU according to its action potential waveforms, which enables separate calculation of every MU's contribution to force. Thus, the muscle force was predicted through a physiologically meaningful muscle force model. In the experiment, HD-SEMG data were recorded from the abductor pollicis brevis muscles of eight healthy subjects during their performance of thumb abduction with the force increasing gradually from zero to four force levels (10%, 20%, 30%, 40% of the maximal voluntary contraction), while the true muscle force was measured simultaneously. When the proposed method was used, the root mean square difference (RMSD) of the error of the estimated force with respect to the measured force was reported to be 8.3% ± 2.8%. The proposed method also significantly outperformed the other four common methods for force estimation (RMSD: from 11.7% to 20%, ), demonstrating its effectiveness. This study offers a useful tool for exploiting the neural drive information towards muscle force estimation with improved precision. The proposed method has wide applications in precise motor control, sport and rehabilitation medicine.

Spatial filtering for enhanced high-density surface electromyographic examination of neuromuscular changes and its application to spinal cord injury

Background

Spatial filtering of multi-channel signals is considered to be an effective pre-processing approach for improving signal-to-noise ratio. The use of spatial filtering for preprocessing high-density (HD) surface electromyogram (sEMG) helps to extract critical spatial information, but its application to non-invasive examination of neuromuscular changes have not been well investigated.

Methods

Aimed at evaluating how spatial filtering can facilitate examination of muscle paralysis, three different spatial filtering methods are presented using principle component analysis (PCA) algorithm, non-negative matrix factorization (NMF) algorithm, and both combination, respectively. Their performance was evaluated in terms of diagnostic power, through HD-sEMG clustering index (CI) analysis of neuromuscular changes in paralyzed muscles following spinal cord injury (SCI).

Results

The experimental results showed that: (1) The CI analysis of conventional single-channel sEMG can reveal complex neuromuscular changes in paralyzed muscles following SCI, and its diagnostic power has been confirmed to be characterized by the variance of Z scores; (2) the diagnostic power was highly dependent on the location of sEMG recording channel. Directly averaging the CI diagnostic indicators over channels just reached a medium level of the diagnostic power; (3) the use of either PCA-based or NMF-based filtering method yielded a greater diagnostic power, and their combination could even enhance the diagnostic power significantly.

Conclusions

This study not only presents an essential preprocessing approach for improving diagnostic power of HD-sEMG, but also helps to develop a standard sEMG preprocessing pipeline, thus promoting its widespread application.

Fluctuations in Human Corticospinal Activity Prior to Grasp

Neuronal firing rate variability prior to movement onset contributes to trial-to-trial variability in primate behavior. However, in humans, whether similar mechanisms contribute to trial-to-trial behavioral variability remains unknown. We investigated the time-course of trial-to-trial variability in corticospinal excitability (CSE) using transcranial magnetic stimulation (TMS) during a self-paced reach-to-grasp task. We hypothesized that CSE variability will be modulated prior to the initiation of reach and that such a modulation would explain trial-to-trial behavioral variability. Able-bodied individuals were visually cued to plan their grip force before exertion of either 30% or 5% of their maximum pinch force capacity on an object. TMS was delivered at six time points (0.5, 0.75, 1, 1.1, 1.2, and 1.3 s) following a visual cue that instructed the force level. We first modeled the relation between CSE magnitude and its variability at rest (n = 12) to study the component of CSE variability pertaining to the task but not related to changes in CSE magnitude (n = 12). We found an increase in CSE variability from 1.2 to 1.3 s following the visual cue at 30% but not at 5% of force. This effect was temporally dissociated from the decrease in CSE magnitude that was observed from 0.5 to 0.75 s following the cue. Importantly, the increase in CSE variability explained at least ∼40% of inter-individual differences in trial-to-trial variability in time to peak force rate. These results were found to be repeatable across studies and robust to different analysis methods. Our findings suggest that the neural mechanisms underlying modulation in CSE variability and CSE magnitude are distinct. Notably, the extent of modulation in variability in corticospinal system prior to grasp within individuals may explain their trial-to-trial behavioral variability.

Quantification of grip strength with complexity analysis of surface electromyogram for hemiplegic post-stroke patients

BACKGROUND

Reduction in grip-strength due to spasticity is a common cause of impairment after stroke.

OBJECTIVE

To find an objective measure of post-stroke spasticity affecting grip-strength through quantification of interaction between antagonist and agonist muscles using complexity analysis of surface electromyogrm (sEMG) signals during isometric grip in healthy and post-stroke participants.

METHODS

The interaction between sEMG signals from antagonist and agonist muscles is quantified through Multiscale-Multivariate-Sample-Entropy (MMSE). This is used to quantify dissimilarity between hands of 12 healthy and 8 post-stroke participants during isometric grip. The clinical relevance of MMSE is explored by examining its correlation with spasticity score i.e. Modified-Ashworth-Scale (MAS). Further, potential of sEMG-based approach to quantify muscle-specific dissimilarity in sEMG activation across hands is investigated in terms of Cepstral-coefficients and power content of sEMG during grip tasks.

RESULTS

Mean MMSE scores of sEMG signals were significantly different (p < 0.05) between paretic and non-paretic hands of Post-stroke participants. High negative correlation was observed between spasticity and complexity scores of paretic hand for post-stroke participants.

CONCLUSIONS

A negative correlation between MAS and MMSE shows higher spasticity can lead to reduced complexity in sEMG. Thus, MMSE based complexity analysis can be used as an indicator of spasticity, affecting grip function.

Stroke-Related Changes in the Complexity of Muscle Activation during Obstacle Crossing Using Fuzzy Approximate Entropy Analysis

This study investigated the complexity of the electromyography (EMG) of lower limb muscles when performing obstacle crossing tasks at different heights in poststroke subjects versus healthy controls. Five poststroke subjects and eight healthy controls were recruited to perform different obstacle crossing tasks at various heights (randomly set at 10, 20, and 30% of the leg’s length). EMG signals were recorded from bilateral biceps femoris (BF), rectus femoris (RF), medial gastrocnemius, and tibialis anterior during obstacle crossing task. The fuzzy approximate entropy (fApEn) approach was used to analyze the complexity of the EMG signals. The fApEn values were significantly smaller in the RF of the trailing limb during the swing phase in poststroke subjects than healthy controls (p < 0.05), which may be an indication of smaller number and less frequent firing rates of the motor units. However, during the swing phase, there were non-significant increases in the fApEn values of BF and RF in the trailing limb of the stroke group compared with those of healthy controls, resulting in a coping strategy when facing challenging tasks. The fApEn values that increased with height were found in the BF of the leading limb during the stance phase and in the RF of the trailing limb during the swing phase (p < 0.05). The reason for this may have been a larger muscle activation associated with the increase in obstacle height. This study demonstrated a suitable and non-invasive method to evaluate muscle function after a stroke.