Is EMG a Viable Alternative to BCI for Detecting Movement Intention in Severe Stroke?

A muscle synergies-based controller to drive a powered upper-limb exoskeleton in reaching tasks

This work introduces a real-time intention decoding algorithm grounded in muscle synergies (Syn-ID). The algorithm detects the electromyographic (EMG) onset and infers the direction of the movement during reaching tasks to control a powered shoulder-elbow exoskeleton. Features related to muscle synergies are used in a Gaussian Mixture Model and probability accumulation-based logic to infer the user's movement direction. The performance of the algorithm was verified by a feasibility study including eight healthy participants. The experiments comprised a transparent session, during which the exoskeleton did not provide any assistance, and an assistive session in which the Syn-ID strategy was employed. Participants were asked to reach eight targets equally spaced on a circumference of 25 cm radius (adjusted chance level: 18.1%). The results showed an average accuracy of 48.7% after 0.6 s from the EMG onset. Most of the confusion of the estimate was found along directions adjacent to the actual one (type 1 error: 33.4%). Effects of the assistance were observed in a statistically significant reduction in the activation of Posterior Deltoid and Triceps Brachii. The final positions of the movements during the assistive session were on average 1.42 cm far from the expected ones, both when the directions were estimated correctly and when type 1 errors occurred. Therefore, combining accurate estimates with type 1 errors, we computed a modified accuracy of 82.10±6.34%. Results were benchmarked with respect to a purely kinematics-based approach. The Syn-ID showed better performance in the first portion of the movement (0.14 s after EMG onset).

Remote Motor Rehabilitation: EMG-IMU based Deep Learning Model Improves the Estimate of Wrist Kinematics

Technology for motor rehabilitation faces challenges in uncontrolled settings, such as at home. In these real-world scenarios, robust signals like electromyographic (EMG) and inertial measurement unit (IMU) data are crucial for decoding continuous human actions. Classical modeling methods, such as linear, adaptive, or static filters, lack the capacity to capture complex relationships between surface EMG and kinematics, as well as generalizability across subjects. We propose a deep learning (DL) model for the continuous decoding of hand motion. Custom-made wearable devices acquired EMG-IMU data from 27 healthy subjects performing a wrist flexion/extension task. Two regression models were compared with a hybrid convolutional neural network and a gated recurrent unit. To address inter-subject variability, a leave-one-subject-out cross-validation approach was implemented. The DL model showed a mean R2 increase of 0.18 compared to the polynomial regressor. Our approach enhances wrist kinematics decoding providing a generalized model based on data captured with wearable devices. The findings hold potential for innovative home-based telemedicine solutions in motor rehabilitation.

A hybrid brain-muscle-machine interface for stroke rehabilitation: Usability and functionality validation in a 2-week intensive intervention

Introduction: The primary constraint of non-invasive brain-machine interfaces (BMIs) in stroke rehabilitation lies in the poor spatial resolution of motor intention related neural activity capture. To address this limitation, hybrid brain-muscle-machine interfaces (hBMIs) have been suggested as superior alternatives. These hybrid interfaces incorporate supplementary input data from muscle signals to enhance the accuracy, smoothness and dexterity of rehabilitation device control. Nevertheless, determining the distribution of control between the brain and muscles is a complex task, particularly when applied to exoskeletons with multiple degrees of freedom (DoFs). Here we present a feasibility, usability and functionality study of a bio-inspired hybrid brain-muscle machine interface to continuously control an upper limb exoskeleton with 7 DoFs. Methods: The system implements a hierarchical control strategy that follows the biologically natural motor command pathway from the brain to the muscles. Additionally, it employs an innovative mirror myoelectric decoder, offering patients a reference model to assist them in relearning healthy muscle activation patterns during training. Furthermore, the multi-DoF exoskeleton enables the practice of coordinated arm and hand movements, which may facilitate the early use of the affected arm in daily life activities. In this pilot trial six chronic and severely paralyzed patients controlled the multi-DoF exoskeleton using their brain and muscle activity. The intervention consisted of 2 weeks of hBMI training of functional tasks with the system followed by physiotherapy. Patients' feedback was collected during and after the trial by means of several feedback questionnaires. Assessment sessions comprised clinical scales and neurophysiological measurements, conducted prior to, immediately following the intervention, and at a 2-week follow-up. Results: Patients' feedback indicates a great adoption of the technology and their confidence in its rehabilitation potential. Half of the patients showed improvements in their arm function and 83% improved their hand function. Furthermore, we found improved patterns of muscle activation as well as increased motor evoked potentials after the intervention. Discussion: This underscores the significant potential of bio-inspired interfaces that engage the entire nervous system, spanning from the brain to the muscles, for the rehabilitation of stroke patients, even those who are severely paralyzed and in the chronic phase.

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.

Evaluation of a novel real-time adaptive assist-as-needed controller for robot-assisted upper extremity rehabilitation following stroke

Rehabilitation therapy plays an essential role in assisting people with stroke regain arm function. Upper extremity robot therapy offers a number of advantages over manual therapies, but can suffer from slacking behavior, where the user lets the robot guide their movements even when they are capable of doing so by themselves, representing a major barrier to reaching the full potential of robot-assist rehabilitation. This is a pilot clinical study that investigates the use of an electromyography-based adaptive assist-as-needed controller to avoid slacking behavior during robotic rehabilitation for people with stroke. The study involved a convenience sample of five individuals with chronic stroke who underwent a robot therapy program utilizing horizontal arm tasks. The Fugl-Meyer assessment (FM) was used to document motor impairment status at baseline. Velocity, time, and position were quantified as performance parameters during the training. Arm and shoulder surface electromyography (EMG) and electroencephalography (EEG) were used to assess the controller's performance. The cross-sectional results showed strong second-order relationships between FM score and outcome measures, where performance metrics (path length and accuracy) were sensitive to change in participants with lower functional status. In comparison, speed, EMG and EEG metrics were more sensitive to change in participants with higher functional status. EEG signal amplitude increased when the robot suggested that the robot was inducing a challenge during the training tasks. This study highlights the importance of multi-sensor integration to monitor and improve upper-extremity robotic therapy.

Challenges of neural interfaces for stroke motor rehabilitation

More than 85% of stroke survivors suffer from different degrees of disability for the rest of their lives. They will require support that can vary from occasional to full time assistance. These conditions are also associated to an enormous economic impact for their families and health care systems. Current rehabilitation treatments have limited efficacy and their long-term effect is controversial. Here we review different challenges related to the design and development of neural interfaces for rehabilitative purposes. We analyze current bibliographic evidence of the effect of neuro-feedback in functional motor rehabilitation of stroke patients. We highlight the potential of these systems to reconnect brain and muscles. We also describe all aspects that should be taken into account to restore motor control. Our aim with this work is to help researchers designing interfaces that demonstrate and validate neuromodulation strategies to enforce a contingent and functional neural linkage between the central and the peripheral nervous system. We thus give clues to design systems that can improve or/and re-activate neuroplastic mechanisms and open a new recovery window for stroke patients.

Empirical Myoelectric Feature Extraction and Pattern Recognition in Hemiplegic Distal Movement Decoding

In myoelectrical pattern recognition (PR), the feature extraction methods for stroke-oriented applications are challenging and remain discordant due to a lack of hemiplegic data and limited knowledge of skeletomuscular function. Additionally, technical and clinical barriers create the need for robust, subject-independent feature generation while using supervised learning (SL). To the best of our knowledge, we are the first study to investigate the brute-force analysis of individual and combinational feature vectors for acute stroke gesture recognition using surface electromyography (EMG) of 19 patients. Moreover, post-brute-force singular vectors were concatenated via a Fibonacci-like spiral net ranking as a novel, broadly applicable concept for feature selection. This semi-brute-force navigated amalgamation in linkage (SNAiL) of EMG features revealed an explicit classification rate performance advantage of 10-17% compared to canonical feature sets, which can drastically extend PR capabilities in biosignal processing.

Control strategies used in lower limb exoskeletons for gait rehabilitation after brain injury: a systematic review and analysis of clinical effectiveness

BACKGROUND

In the past decade, there has been substantial progress in the development of robotic controllers that specify how lower-limb exoskeletons should interact with brain-injured patients. However, it is still an open question which exoskeleton control strategies can more effectively stimulate motor function recovery. In this review, we aim to complement previous literature surveys on the topic of exoskeleton control for gait rehabilitation by: (1) providing an updated structured framework of current control strategies, (2) analyzing the methodology of clinical validations used in the robotic interventions, and (3) reporting the potential relation between control strategies and clinical outcomes.

METHODS

Four databases were searched using database-specific search terms from January 2000 to September 2020. We identified 1648 articles, of which 159 were included and evaluated in full-text. We included studies that clinically evaluated the effectiveness of the exoskeleton on impaired participants, and which clearly explained or referenced the implemented control strategy.

RESULTS

(1) We found that assistive control (100% of exoskeletons) that followed rule-based algorithms (72%) based on ground reaction force thresholds (63%) in conjunction with trajectory-tracking control (97%) were the most implemented control strategies. Only 14% of the exoskeletons implemented adaptive control strategies. (2) Regarding the clinical validations used in the robotic interventions, we found high variability on the experimental protocols and outcome metrics selected. (3) With high grade of evidence and a moderate number of participants (N = 19), assistive control strategies that implemented a combination of trajectory-tracking and compliant control showed the highest clinical effectiveness for acute stroke. However, they also required the longest training time. With high grade of evidence and low number of participants (N = 8), assistive control strategies that followed a threshold-based algorithm with EMG as gait detection metric and control signal provided the highest improvements with the lowest training intensities for subacute stroke. Finally, with high grade of evidence and a moderate number of participants (N = 19), assistive control strategies that implemented adaptive oscillator algorithms together with trajectory-tracking control resulted in the highest improvements with reduced training intensities for individuals with chronic stroke.

CONCLUSIONS

Despite the efforts to develop novel and more effective controllers for exoskeleton-based gait neurorehabilitation, the current level of evidence on the effectiveness of the different control strategies on clinical outcomes is still low. There is a clear lack of standardization in the experimental protocols leading to high levels of heterogeneity. Standardized comparisons among control strategies analyzing the relation between control parameters and biomechanical metrics will fill this gap to better guide future technical developments. It is still an open question whether controllers that provide an on-line adaptation of the control parameters based on key biomechanical descriptors associated to the patients' specific pathology outperform current control strategies.

A sequential learning model with GNN for EEG-EMG-based stroke rehabilitation BCI

Introduction

Brain-computer interfaces (BCIs) have the potential in providing neurofeedback for stroke patients to improve motor rehabilitation. However, current BCIs often only detect general motor intentions and lack the precise information needed for complex movement execution, mainly due to insufficient movement execution features in EEG signals.

Methods

This paper presents a sequential learning model incorporating a Graph Isomorphic Network (GIN) that processes a sequence of graph-structured data derived from EEG and EMG signals. Movement data are divided into sub-actions and predicted separately by the model, generating a sequential motor encoding that reflects the sequential features of the movements. Through time-based ensemble learning, the proposed method achieves more accurate prediction results and execution quality scores for each movement.

Results

A classification accuracy of 88.89% is achieved on an EEG-EMG synchronized dataset for push and pull movements, significantly outperforming the benchmark method's performance of 73.23%.

Discussion

This approach can be used to develop a hybrid EEG-EMG brain-computer interface to provide patients with more accurate neural feedback to aid their recovery.

EEG-EMG hybrid real-time classification of hand grasp and release movements intention in chronic stroke patients

Rehabilitation of the hand motor function is essential for stroke patients to resume activities of daily living. Recent studies have shown that wearable robot systems, like a multi degree-of-freedom soft glove, have the potential to improve hand motor impairment. The rehabilitation system, which is intuitively controlled according to the user's intention, is expected to induce active participation of the user and further promote brain plasticity. However, due to the patient-specific nature of stroke patients, extracting the intention from stroke patients is still challenging. In this study, we implemented a classifier that combines EEG and EMG to detect chronic stroke patients' four types of intention: rest, grasp, hold, and release. Three chronic stroke patients participated in the experiment and performed rest, grasp, hold, and release actions. The rest vs. grasp binary classifier and release vs. hold binary classifier showed 76.9% and 86.6% classification accuracy in real-time, respectively. In addition, patient-specific accuracy comparisons showed that the hybrid approach was robust to upper limb impairment level compared to other approaches. We believe that these results could pave the way for the development of BCI-based robotic hand rehabilitation therapy.

Real-Time Control of a Multi-Degree-of-Freedom Mirror Myoelectric Interface During Functional Task Training

Motor learning mediated by motor training has in the past been explored for rehabilitation. Myoelectric interfaces together with exoskeletons allow patients to receive real-time feedback about their muscle activity. However, the number of degrees of freedom that can be simultaneously controlled is limited, which hinders the training of functional tasks and the effectiveness of the rehabilitation therapy. The objective of this study was to develop a myoelectric interface that would allow multi-degree-of-freedom control of an exoskeleton involving arm, wrist and hand joints, with an eye toward rehabilitation. We tested the effectiveness of a myoelectric decoder trained with data from one upper limb and mirrored to control a multi-degree-of-freedom exoskeleton with the opposite upper limb (i.e., mirror myoelectric interface) in 10 healthy participants. We demonstrated successful simultaneous control of multiple upper-limb joints by all participants. We showed evidence that subjects learned the mirror myoelectric model within the span of a five-session experiment, as reflected by a significant decrease in the time to execute trials and in the number of failed trials. These results are the necessary precursor to evaluating if a decoder trained with EMG from the healthy limb could foster learning of natural EMG patterns and lead to motor rehabilitation in stroke patients.

Predicting manual wheelchair initiation movement with EMG activity during over ground propulsion

Context/Objective: This is a preliminary study of movement finalities prediction in manual wheelchairs (MWCs) from electromyography (EMG) data. MWC users suffer from musculoskeletal disorders and need assistance while moving. The purpose of this work is to predict the direction and speed of movement in MWCs from EMG data prior to movement initiation. This prediction could be used by MWC to assist users in their displacement by doing a smart electrical assistance based on displacement prediction.Design: Experimental study.Setting: Trained Subject LAMIH Laboratory.Participants: Eight healthy subjects trained to move in manual wheelchairs.Interventions: Subjects initiated the movement in three directions (front, right and left) and with two speeds (maximum speed and spontaneous speed) from two hand positions (on the thighs or on the handrim). A total of 96 movements was studied. Activation of 14 muscles was recorded bilaterally at the deltoid anterior, deltoid posterior, biceps brachii, pectoralis major, rectus abdominis, obliquus externus and erector spinae.Outcome Measures: Prior amplitude, prior time and anticipatory postural adjustments were measured. A hierarchical multi-class classification using logistic regression was used to create a cascade of prediction models. We performed a stepwise (forward-backward) selection of variables using the Bayesian information criterion. Percentages of well-classified movements have been measured through the means of a cross-validation.Results: Prediction is possible using the EMG parameters and allows to discriminate the direction / speed combination with 95% correct classification on the 6 possible classes (3 directions * 2 speeds).Conclusion: Action planning in the static position showed significant adaptability to the forthcoming parameters displacement. The percentages of prediction presented in this work make it possible to envision an intuitive assistance to the initiation of the MWC displacement adapted to the user's intentions.

Biopotential Signal Monitoring Systems in Rehabilitation: A Review

Monitoring physical activity in medical and clinical rehabilitation, in sports environments or as a wellness indicator is helpful to measure, analyze and evaluate physiological parameters involving the correct subject’s movements. Thanks to integrated circuit (IC) technologies, wearable sensors and portable devices have expanded rapidly in monitoring physical activities in sports and tele-rehabilitation. Therefore, sensors and signal acquisition devices became essential in the tele-rehabilitation path to obtain accurate and reliable information by analyzing the acquired physiological signals. In this context, this paper provides a state-of-the-art review of the recent advances in electroencephalogram (EEG), electrocardiogram (ECG) and electromyogram (EMG) signal monitoring systems and sensors that are relevant to the field of tele-rehabilitation and health monitoring. Mostly, we focused our contribution in EMG signals to highlight its importance in rehabilitation context applications. This review focuses on analyzing the implementation of sensors and biomedical applications both in literature than in commerce. Moreover, a final review discussion about the analyzed solutions is also reported at the end of this paper to highlight the advantages of physiological monitoring systems in rehabilitation and individuate future advancements in this direction. The main contributions of this paper are (i) the presentation of interesting works in the biomedical area, mainly focusing on sensors and systems for physical rehabilitation and health monitoring between 2016 and up-to-date, and (ii) the indication of the main types of commercial sensors currently being used for biomedical applications.

Prediction of Myoelectric Biomarkers in Post-Stroke Gait

Electromyography (EMG) is sensitive to neuromuscular changes resulting from ischemic stroke and is considered a potential predictive tool of post-stroke gait and rehabilitation management. This study aimed to evaluate the potential myoelectric biomarkers for the classification of stroke-impaired muscular activity of the stroke patient group and the muscular activity of the control healthy adult group. We also proposed an EMG-based gait monitoring system consisting of a portable EMG device, cloud-based data processing, data analytics, and a health advisor service. This system was investigated with 48 stroke patients (mean age 70.6 years, 65% male) admitted into the emergency unit of a hospital and 75 healthy elderly volunteers (mean age 76.3 years, 32% male). EMG was recorded during walking using the portable device at two muscle positions: the bicep femoris muscle and the lateral gastrocnemius muscle of both lower limbs. The statistical result showed that the mean power frequency (MNF), median power frequency (MDF), peak power frequency (PKF), and mean power (MNP) of the stroke group differed significantly from those of the healthy control group. In the machine learning analysis, the neural network model showed the highest classification performance (precision: 88%, specificity: 89%, accuracy: 80%) using the training dataset and highest classification performance (precision: 72%, specificity: 74%, accuracy: 65%) using the testing dataset. This study will be helpful to understand stroke-impaired gait changes and decide post-stroke rehabilitation.

Intention Prediction and Human Health Condition Detection in Reaching Tasks with Machine Learning Techniques

Detecting human motion and predicting human intentions by analyzing body signals are challenging but fundamental steps for the implementation of applications presenting human–robot interaction in different contexts, such as robotic rehabilitation in clinical environments, or collaborative robots in industrial fields. Machine learning techniques (MLT) can face the limit of small data amounts, typical of this kind of applications. This paper studies the illustrative case of the reaching movement in 10 healthy subjects and 21 post-stroke patients, comparing the performance of linear discriminant analysis (LDA) and random forest (RF) in: (i) predicting the subject’s intention of moving towards a specific direction among a set of possible choices, (ii) detecting if the subject is moving according to a healthy or pathological pattern, and in the case of discriminating the damage location (left or right hemisphere). Data were captured with wearable electromagnetic sensors, and a sub-section of the acquired signals was required for the analyses. The possibility of detecting with which arm (left or right hand) the motion was performed, and the sensitivity of the MLT to variations in the length of the signal sub-section were also evaluated. LDA and RF prediction accuracies were compared: Accuracy improves when only healthy subjects or longer signals portions are considered up to 11% and at least 10%, respectively. RF reveals better estimation performance both as intention predictor (on average 59.91% versus the 62.19% of LDA), and health condition detector (over 90% in all the tests).

Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation

Brain-computer interfaces (BCIs) provide a unique technological solution to circumvent the damaged motor system. For neurorehabilitation, the BCI can be used to translate neural signals associated with movement intentions into tangible feedback for the patient, when they are unable to generate functional movement themselves. Clinical interest in BCI is growing rapidly, as it would facilitate rehabilitation to commence earlier following brain damage and provides options for patients who are unable to partake in traditional physical therapy. However, substantial challenges with existing BCI implementations have prevented its widespread adoption. Recent advances in knowledge and technology provide opportunities to facilitate a change, provided that researchers and clinicians using BCI agree on standardisation of guidelines for protocols and shared efforts to uncover mechanisms. We propose that addressing the speed and effectiveness of learning BCI control are priorities for the field, which may be improved by multimodal or multi-stage approaches harnessing more sensitive neuroimaging technologies in the early learning stages, before transitioning to more practical, mobile implementations. Clarification of the neural mechanisms that give rise to improvement in motor function is an essential next step towards justifying clinical use of BCI. In particular, quantifying the unknown contribution of non-motor mechanisms to motor recovery calls for more stringent control conditions in experimental work. Here we provide a contemporary viewpoint on the factors impeding the scalability of BCI. Further, we provide a future outlook for optimal design of the technology to best exploit its unique potential, and best practices for research and reporting of findings.

Development of a Low-Cost EEG-Controlled Hand Exoskeleton 3D Printed on Textiles

Stroke survivors can be affected by motor deficits in the hand. Robotic equipment associated with brain–machine interfaces (BMI) may aid the motor rehabilitation of these patients. BMIs involving orthotic control by motor imagery practices have been successful in restoring stroke patients' movements. However, there is still little acceptance of the robotic devices available, either by patients and clinicians, mainly because of the high costs involved. Motivated by this context, this work aims to design and construct the Hand Exoskeleton for Rehabilitation Objectives (HERO) to recover extension and flexion movements of the fingers. A three-dimensional (3D) printing technique in association with textiles was used to produce a lightweight and wearable device. 3D-printed actuators have also been designed to reduce equipment costs. The actuator transforms the torque of DC motors into linear force transmitted by Bowden cables to move the fingers passively. The exoskeleton was controlled by neuroelectric signal—electroencephalography (EEG). Concept tests were performed to evaluate control performance. A healthy volunteer was submitted to a training session with the exoskeleton, according to the Graz-BCI protocol. Ergonomy was evaluated with a two-dimensional (2D) tracking software and correlation analysis. HERO can be compared to ordinary clothing. The weight over the hand was around 102 g. The participant was able to control the exoskeleton with a classification accuracy of 91.5%. HERO project resulted in a lightweight, simple, portable, ergonomic, and low-cost device. Its use is not restricted to a clinical setting. Thus, users will be able to execute motor training with the HERO at hospitals, rehabilitation clinics, and at home, increasing the rehabilitation intervention time. This may support motor rehabilitation and improve stroke survivors life quality.

Performance Evaluation of a Wearable Tattoo Electrode Suitable for High-Resolution Surface Electromyogram Recording

OBJECTIVE

High-density surface electromyography (HD-sEMG) has been utilized extensively in neuromuscular research. Despite its potential advantages, limitations in electrode design have largely prevented widespread acceptance of the technology. Commercial electrodes have limited spatial fidelity, because of a lack of sharpness of the signal, and variable signal stability. We demonstrate here a novel tattoo electrode that addresses these issues. Our dry HD electrode grid exhibits remarkable deformability which ensures superior conformity with the skin surface, while faithfully recording signals during different levels of muscle contraction.

METHOD

We fabricated a 4 cm×3 cm tattoo HD electrode grid on a stretchable electronics membrane for sEMG applications. The grid was placed on the skin overlying the biceps brachii of healthy subjects, and was used to record signals for several hours while tracking different isometric contractions.

RESULTS

The sEMG signals were recorded successfully from all 64 electrodes across the grid. These electrodes were able to faithfully record sEMG signals during repeated contractions while maintaining a stable baseline at rest. During voluntary contractions, broad EMG frequency content was preserved, with accurate reproduction of the EMG spectrum across the full signal bandwidth.

CONCLUSION

The tattoo grid electrode can potentially be used for recording high-density sEMG from skin overlying major limb muscles. Layout programmability, good signal quality, excellent baseline stability, and easy wearability make this electrode a potentially valuable component of future HD electrode grid applications.

SIGNIFICANCE

The tattoo electrode can facilitate high fidelity recording in clinical applications such as tracking the evolution and time-course of challenging neuromuscular degenerative disorders.

Force Decoding of Caudal Forelimb Area and Rostral Forelimb Area in Chronic Stroke Rats

Brain machine interfaces (BMIs) used for movement restoration primarily rely on studies of motor decoding. It has been proved that local field potentials (LFPs) from primary motor cortex and premotor cortex of normal rodents could be used for decoding motor signals. However, few studies have explored the decoding performance of these brain areas under motor cortex damage. In this work, we focus on force decoding performance of LFPs spectrum from both ipsilesional caudal forelimb area (CFA) and rostral forelimb area (RFA) of rodents with ischemia over CFA. After three months of ischemia induced by photothrombosis over CFA, the power of high-frequency bands (>120 Hz) from both CFA and RFA can decode force signals by Kalman filters. The fair performance of CFA indicates motor reorganization over penumbra. Further exploration of RFA decoding ability proves that at least four electrodes of RFA should be used on decoding and electrodes far from CFA of stroke rats could achieve almost as good results as those close to CFA of normal rats, which indicates the motor remapping. Experimental results show the long-term stability of PM LFPs decoding performance of stroke rats as the trained Kalman model could be used to accurately decode force some days later which provides a possibility for online decoding system. In conclusion, our work shows that even under CFA ischemia, high-frequency power of LFPs from RFA is still able to accurately decode force signals and has long stability, which provides the possibility of BMIs for motor function reconstruction of chronic stroke patients.

Decoding of Ankle Joint Movements in Stroke Patients Using Surface Electromyography

Stroke is a cerebrovascular disease (CVD), which results in hemiplegia, paralysis, or death. Conventionally, a stroke patient requires prolonged sessions with physical therapists for the recovery of motor function. Various home-based rehabilitative devices are also available for upper limbs and require minimal or no assistance from a physiotherapist. However, there is no clinically proven device available for functional recovery of a lower limb. In this study, we explored the potential use of surface electromyography (sEMG) as a controlling mechanism for the development of a home-based lower limb rehabilitative device for stroke patients. In this experiment, three channels of sEMG were used to record data from 11 stroke patients while performing ankle joint movements. The movements were then decoded from the sEMG data and their correlation with the level of motor impairment was investigated. The impairment level was quantified using the Fugl-Meyer Assessment (FMA) scale. During the analysis, Hudgins time-domain features were extracted and classified using linear discriminant analysis (LDA) and artificial neural network (ANN). On average, 63.86% ± 4.3% and 67.1% ± 7.9% of the movements were accurately classified in an offline analysis by LDA and ANN, respectively. We found that in both classifiers, some motions outperformed others (p < 0.001 for LDA and p = 0.014 for ANN). The Spearman correlation (ρ) was calculated between the FMA scores and classification accuracies. The results indicate that there is a moderately positive correlation (ρ = 0.75 for LDA and ρ = 0.55 for ANN) between the two of them. The findings of this study suggest that a home-based EMG system can be developed to provide customized therapy for the improvement of functional lower limb motion in stroke patients.

Decoding Attempted Hand Movements in Stroke Patients Using Surface Electromyography

Brain- and muscle-triggered exoskeletons have been proposed as a means for motor training after a stroke. With the possibility of performing different movement types with an exoskeleton, it is possible to introduce task variability in training. It is difficult to decode different movement types simultaneously from brain activity, but it may be possible from residual muscle activity that many patients have or quickly regain. This study investigates whether nine different motion classes of the hand and forearm could be decoded from forearm EMG in 15 stroke patients. This study also evaluates the test-retest reliability of a classical, but simple, classifier (linear discriminant analysis) and advanced, but more computationally intensive, classifiers (autoencoders and convolutional neural networks). Moreover, the association between the level of motor impairment and classification accuracy was tested. Three channels of surface EMG were recorded during the following motion classes: Hand Close, Hand Open, Wrist Extension, Wrist Flexion, Supination, Pronation, Lateral Grasp, Pinch Grasp, and Rest. Six repetitions of each motion class were performed on two different days. Hudgins time-domain features were extracted and classified using linear discriminant analysis and autoencoders, and raw EMG was classified with convolutional neural networks. On average, 79 ± 12% and 80 ± 12% (autoencoders) of the movements were correctly classified for days 1 and 2, respectively, with an intraclass correlation coefficient of 0.88. No association was found between the level of motor impairment and classification accuracy (Spearman correlation: 0.24). It was shown that nine motion classes could be decoded from residual EMG, with autoencoders being the best classification approach, and that the results were reliable across days; this may have implications for the development of EMG-controlled exoskeletons for training in the patient’s home.

Different sEMG and EEG Features Analysis for Gait phase Recognition

This research focuses on the gait phase recognition using different sEMG and EEG features. Seven healthy volunteers, 23-26 years old, were enrolled in this experiment. Seven phases of gait were divided by three-dimensional trajectory of lower limbs during treadmill walking and classified by Library for Support Vector Machines (LIBSVM). These gait phases include loading response, mid-stance, terminal Stance, pre-swing, initial swing, mid-swing, and terminal swing. Different sEMG and EEG features were assessed in this study. Gait phases of three kinds of walking speed were analyzed. Results showed that the slope sign change (SSC) and mean power frequency (MPF) of sEMG signals and SSC of EEG signals achieved higher accuracy of gait phase recognition than other features, and the accuracy are 95.58% (1.4 km/h), 97.63% (2.0 km/h) and 98.10% (2.6 km/h) respectively. Furthermore, the accuracy of gait phase recognition in the speed of 2.6 km/h is better than other walking speeds.

Processing Surface EMG Signals for Exoskeleton Motion Control

The surface electromyography (sEMG) signal has been used for volitional control of robotic assistive devices. There are still challenges in improving system performance accuracy and signal processing to remove systematic noise. This study presents procedures and a pilot validation of the EMG-driven speed-control of exoskeleton and integrated treadmill with a goal to provide better interaction between a user and the system. The gait cycle duration (GCD) was extracted from sEMG signals using the autocorrelation algorithm and Bayesian fusion algorithm. GCDs of various walking speeds were then programmed to control the motion speed of exoskeleton robotic system. The performance and efficiency of this sEMG-controlled robotic assistive ambulation system was tested and validated among 6 healthy volunteers. The results demonstrated that the autocorrelation algorithm extracted the GCD from individual muscle contraction. The GCDs of individual muscles had variability between different walking steps under a designated walking speed. Bayesian fusion algorithms processed the GCDs of multiple muscles yielding a final GCD with the least variance. The fused GCD effectively controlled the motion speeds of exoskeleton and treadmill. The higher amplitude of EMG signals with shorter GCD was found during a faster walking speed. The algorithms using fused GCDs and gait stride length yielded trajectory joint motion tracks in a shape of sine curve waveform. The joint angles of the exoskeleton measured by a decoder mounted on the hip turned out to be in sine waveforms. The hip joint motion track of the exoskeleton matched the angles projected by trajectory curve generated by computer algorithms based on the fused GCDs with high agreement. The EMG-driven speed-control provided the human-machine inter-limb coordination mechanisms for an intuitive speed control of the exoskeleton-treadmill system at the user's intents. Potentially the whole system can be used for gait rehabilitation of incomplete spinal cord hemispheric stroke patients as goal-directed and task-oriented training tool.

EMG- Versus EEG-Triggered Electrical Stimulation for Inducing Corticospinal Plasticity

Brain-computer interfaces have been proposed for stroke rehabilitation. Motor cortical activity derived from the electroencephalography (EEG) can trigger external devices that provide congruent sensory feedback. However, many stroke patients regain residual muscle (EMG: electromyography) control due to spontaneous recovery and rehabilitation; therefore, EEG may not be necessary as a control signal. In this paper, a direct comparison was made between the induction of corticospinal plasticity using either EEG- or EMG-controlled electrical nerve stimulation. Twenty healthy participants participated in two intervention sessions consisting of EEG- and EMG-controlled electrical stimulation. The sessions consisted of 50 pairings between foot dorsiflexion movements (decoded through either EEG or EMG) and electrical stimulation of the common peroneal nerve. Before, immediately after and 30 minutes after the intervention, 15 motor evoked potentials (MEPs) were elicited in tibialis anterior through transcranial magnetic stimulation. Increased MEPs were observed immediately after (62 ± 26%, 73 ± 27% for EEG- and EMG-triggered electrical stimulation, respectively) and 30 minutes after each of the two interventions (79 ± 26% and 72 ± 27%) compared to the pre-intervention measurement. There was no difference between the interventions. Both EEG- and EMG-controlled electrical stimulation can induce corticospinal plasticity which suggests that stroke patients with residual EMG can use that modality instead of EEG to trigger stimulation.

On the design of EEG-based movement decoders for completely paralyzed stroke patients

Background

Brain machine interface (BMI) technology has demonstrated its efficacy for rehabilitation of paralyzed chronic stroke patients. The critical component in BMI-training consists of the associative connection (contingency) between the intention and the feedback provided. However, the relationship between the BMI design and its performance in stroke patients is still an open question.

Methods

In this study we compare different methodologies to design a BMI for rehabilitation and evaluate their effects on movement intention decoding performance. We analyze the data of 37 chronic stroke patients who underwent 4 weeks of BMI intervention with different types of association between their brain activity and the proprioceptive feedback. We simulate the pseudo-online performance that a BMI would have under different conditions, varying: (1) the cortical source of activity (i.e., ipsilesional, contralesional, bihemispheric), (2) the type of spatial filter applied, (3) the EEG frequency band, (4) the type of classifier; and also evaluated the use of residual EMG activity to decode the movement intentions.

Results

We observed a significant influence of the different BMI designs on the obtained performances. Our results revealed that using bihemispheric beta activity with a common average reference and an adaptive support vector machine led to the best classification results. Furthermore, the decoding results based on brain activity were significantly higher than those based on muscle activity.

Conclusions

This paper underscores the relevance of the different parameters used to decode movement, using EEG in severely paralyzed stroke patients. We demonstrated significant differences in performance for the different designs, which supports further research that should elucidate if those approaches leading to higher accuracies also induce higher motor recovery in paralyzed stroke patients.