EMG Channels Research Articles

Repetitive muscle silent periods in acute post-anoxic brain injury: A novel phenotype of negative myoclonus

To report a novel phenotype of negative myoclonus in acute post-anoxic brain injury (PABI). We performed a retrospective analysis of 18-channel video-EEG and surface-EMG (sEMG) recordings of three patients with PABI. sEMG electrodes were placed on the neck, bulbar and arm muscles. All three patients had whole body tonic posturing with intermittent brief relaxation. In patients #1 and #2, a generalized EEG burst-suppression was present. Repetitive silent periods (SPs) were noted in the sEMG channels, time-locked to EEG bursts. The bursts preceded the SPs by 135ms and 124ms, respectively. The average SP duration was 910ms and 852ms in patients #1 and 2, respectively. Patient #3 had a generalized background suppression pattern and average SP duration of 272.5ms. The SP recruitment pattern in patient #1 was rostro-caudal whereas patient #3 had a variable recruitment pattern. Acute post-anoxic negative myoclonus can be detected in comatose patients with sEMG electrodes. The muscle SPs produce intermittent relaxation of the tonic posturing. The putative generator can be cortical or reticular, similar to Lance-Adams syndrome. We describe a novel phenotype of negative myoclonus in acute PABI. We also describe the EEG and sEMG characteristics and the localization of the putative generator.

Evaluation of Finger Movement Impairment Level Recognition Method Based on Fugl-Meyer Assessment Using Surface EMG

Subjectivity has been an inherent issue in the conventional Fugl-Meyer assessment, which has been the focus of impairment-level recognition in several studies. This study continues our previous work on the use of EMG to recognize finger movement impairment levels. In contrast to our previous work, this study provided a better and more reliable recognition result with improved experimental settings, such as an increased sampling frequency, EMG channels, and extensive patient data. This study employed two data processing mechanisms, inter-subject cross-validation (ISCV) and data-scaled inter-subject cross-validation (DS-ISCV), resulting in two evaluation methods. The machine learning algorithms employed in this study were SVM, random forest (RF), and multi-layer perceptron (MLP). MLP_ISCV achieved the highest average recall score of 0.73 across impairment levels in the spherical grasp task. Subsequently, the highest average recall score of 0.72 among non-majority classes was achieved by SVM_DS-ISCV in the mass extension task. The cross-validation result shows that the proposed method effectively handled the imbalanced dataset without being biased toward the majority class. The proposed method demonstrated the potential to assist doctors in clarifying the subjective assessment of finger movement impairment levels.

Detection of Sub-Clinical Tarsal Tunnel Syndrome in Rheumatoid Arthritis Patients

Abstract Background Asymptomatic or subclinical entrapments are common and frequent specially in patients with rheumatoid arthritis. Objectives Detection of sub clinical tarsal tunnel syndrome in rheumatoid arthritis patients and detect its correlation with disease activity and severity. Patient and Methods This case control was carried out on 16 patients with rheumatoid arthritis (RA) diagnosed according to American College of Rheumatology (ACR) and European League Against Rheumatism (EULAR) criteria of 2010 and 16 healthy controls matched for age and sex. Assessment of disease activity was done by Disease Activity Score in 28 joints (DAS28) and Rheumatoid Arthritis Severity Scale (RASS). Electrophysiological studies were conducted on a two channel EMG machine, for each patient on both legs. Results No significant differences were noted between study groups regarding basal demographic and clinical manifestations. In rheumatoid patient, mean ± SD for disease activity score (DAS) was 4.11±0.51 versus 2.51 ± 0.66 in control patient with p < 0.0001. Regarding RASS, in rheumatoid patient, mean ± SD for disease activity was 28.94 ± 5.78; mean ± SD for functional impairment was 28.44 ± 5.46 and mean ± SD for physical damage was 31.75 ± 5.58 versus 11.44 ± 4.91, 13.88 ± 5.88 and 12.69 ± 4.24 in control group with statistically significant difference between both groups (p < 0.0001). In rheumatoid cases, all patients (100%) were affected on medial plantar nerve “sensory latency and amplitude” of right and left sides, also, all patients (100%) were affected on lateral plantar nerve “sensory latency” of right and left sides, while for amplitude right and left; 31.25% of patients were affected. Medial plantar nerves (motor latency) in both sides weren‟t affected in any patients, while for amplitude and nerve conduction velocity the percentage of patients in right and left was 100%. Lateral plantar nerve (motor latency and amplitude) weren‟t affected in any patients in both sides, while for nerve conduction velocity the percentage of patients in right and left was 100%. Posterior tibial nerves (motor latency and nerve conduction velocity) weren‟t affected in any patients in both sides, while for amplitude the percentage of patients in right and left was 6.25%. No correlation was detected between DAS and RASS as regard clinical, laboratory data and electrophysiological study in rheumatoid cases. This was clearly demonstrated by sensitivity and specificity test that medial and lateral plantar nerved (sensory and motor) were of highly significant value and posterior tibial nerve (motor) was of insignificant value in detection of sub clinical tarsal tunnel syndrome in rheumatoid arthritis patients. Conclusion Subclinical entrapments and sub clinical tarsal tunnel syndrome were detected in all rheumatoid arthritis patients using electrophysiological study and they were positively correlated with disease activity and severity. Also, most of joint complaints of rheumatoid arthritis patients as oedema and pain were due undiagnosed subclinical entrapments and sub clinical tarsal tunnel syndrome.

Comparative Study of sEMG Feature Evaluation Methods Based on the Hand Gesture Classification Performance.

Effective feature extraction and selection are crucial for the accurate classification and prediction of hand gestures based on electromyographic signals. In this paper, we systematically compare six filter and wrapper feature evaluation methods and investigate their respective impacts on the accuracy of gesture recognition. The investigation is based on several benchmark datasets and one real hand gesture dataset, including 15 hand force exercises collected from 14 healthy subjects using eight commercial sEMG sensors. A total of 37 time- and frequency-domain features were extracted from each sEMG channel. The benchmark dataset revealed that the minimum Redundancy Maximum Relevance (mRMR) feature evaluation method had the poorest performance, resulting in a decrease in classification accuracy. However, the RFE method demonstrated the potential to enhance classification accuracy across most of the datasets. It selected a feature subset comprising 65 features, which led to an accuracy of 97.14%. The Mutual Information (MI) method selected 200 features to reach an accuracy of 97.38%. The Feature Importance (FI) method reached a higher accuracy of 97.62% but selected 140 features. Further investigations have shown that selecting 65 and 75 features with the RFE methods led to an identical accuracy of 97.14%. A thorough examination of the selected features revealed the potential for three additional features from three specific sensors to enhance the classification accuracy to 97.38%. These results highlight the significance of employing an appropriate feature selection method to significantly reduce the number of necessary features while maintaining classification accuracy. They also underscore the necessity for further analysis and refinement to achieve optimal solutions.

Signal to noise ratio of masticatory muscle activity of functional and non-functional oral tasks.

Electromyographic activity (EMG) of masticatory muscles during wakefulness is understudied. It is unclear if single channel ambulatory EMG devices are sensitive enough to detect masticatory muscle activity (MMA) during wakefulness. To compare the MMA of various oral tasks recorded with a single channel EMG device ((Grindcare4-datalogger Prototype device) (GC4-β)) and a conventional EMG (cEMG) device. EMG activity of 30 standardised oral tasks was recorded unilaterally from the masseter and anterior temporalis muscle in 24 healthy volunteers using GC4-β and a cEMG device. To compare the EMG data, signal-to-noise ratios (SNR) were calculated as a way to normalise EMG activity across tasks. Analysis of variance was used to compare the SNR between the devices, muscles and oral tasks. SNR measured from GC4-β was overall significantly higher than the cEMG device (p =.001). The SNR for maximum voluntary contraction (MVC) was significantly higher than all other tasks (p <.001). SNR for temporalis with GC4-β was significantly higher for MVC, hard food, soft food, gum chewing (dominant side), rhythmic clenching and upper lip biting compared to the cEMG device (p <.021). The SNR for masseter with GC4-β was significantly higher for hard food and gum chewing (dominant side), rhythmic clenching, rhythmic biting of an object and yawning compared to the cEMG device (p <.022). This study provides novel insight into the EMG patterns of numerous oral tasks enhancing knowledge of physiological differences between the masticatory muscles. Further, single channel EMG devices can effectively measure the EMG activity of various oral tasks during wakefulness.

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

Myoelectric pattern recognition systems have shown promising control of upper limb powered prostheses and are now commercially available. These pattern recognition systems typically record from up to 8 muscle sites, whereas other control systems use two-site control. While previous offline studies have shown 8 or fewer sites to be optimal, real-time control was not evaluated. Six individuals with no limb absence and four individuals with a transradial amputation controlled a virtual upper limb prosthesis using pattern recognition control with 8 and 16 channels of EMG. Additionally, two of the individuals with a transradial amputation performed the Assessment for Capacity of Myoelectric Control (ACMC) with a multi-articulating hand and wrist prosthesis with the same channel count conditions. Users had significant improvements in control when using 16 compared to 8 EMG channels including decreased classification error (p = 0.006), decreased completion time (p = 0.019), and increased path efficiency (p = 0.013) when controlling a virtual prosthesis. ACMC scores increased by more than three times the minimal detectable change from the 8 to the 16-channel condition. The results of this study indicate that increasing EMG channel count beyond the clinical standard of 8 channels can benefit myoelectric pattern recognition users.

Motion intention recognition of the affected hand based on the sEMG and improved DenseNet network

The key to sEMG (surface electromyography)-based control of robotic hands is the utilization of sEMG signals from the affected hand of amputees to infer their motion intentions. With the advancements in deep learning, researchers have successfully developed viable solutions for CNN (Convolutional Neural Network)-based gesture recognition. However, most studies have primarily concentrated on utilizing sEMG data from the hands of healthy subjects, often relying on high-dimensional feature vectors obtained from a substantial number of electrodes. This approach has yielded high-performing sEMG recognition systems but has failed to consider the considerable inconvenience that the abundance of electrodes poses to the daily lives and work of patients. In this paper, we focused on transradial amputees and used sEMG data from the Ninapro DB3 database as our dataset. Firstly, we introduce a STFT (Short-Time Fourier Transform)-based time-frequency feature fusion map for sEMG. This map includes both time-frequency features and the time-frequency localization of sEMG signals. Secondly, we propose an Improved DenseNet (Dense Convolutional Network) model for recognizing motion intentions in the affected hand of amputees based on their sEMG signals. Finally, addressing the issue of optimizing the number of electrodes carried by amputees, we introduce the PCMIRR (Pearson Correlation and Motion Intention Recognition Rate) algorithm. This algorithm optimizes the number of channels by considering the Pearson correlation between the sEMG channels of amputees and the recognition rate of motion intentions in the affected hand based on single-channel sEMG data. The experimental results reveal that the recognition accuracy, recall, and F1 score achieved by the Improved DenseNet model were 93.82%, 93.61%, and 93.65%, respectively. When the number of electrodes was optimized to 8, the recognition accuracy reached 94.50%. In summary, this paper ultimately attained precise recognition of motion intentions in amputees' affected hands while utilizing the minimum number of sEMG channels. This method offers a novel approach to sEMG-based control of bionic robotic hands.

Explainable AI-Guided Optimization of EMG Channels and Features for Precise Hand Gesture Classification: a SHAP-Based Study

Explainable AI-Guided Optimization of EMG Channels and Features for Precise Hand Gesture Classification: a SHAP-Based Study

A surface electromyography based hand gesture recognition framework leveraging variational mode decomposition technique and deep learning classifier

A surface electromyography based hand gesture recognition framework leveraging variational mode decomposition technique and deep learning classifier

Myoelectric Model Reference Adaptive Control with Adaptive Kalman Filter for a soft elbow exoskeleton

Myoelectric Model Reference Adaptive Control with Adaptive Kalman Filter for a soft elbow exoskeleton

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

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

Gender Differences in Taste Sensations Based on Frequency Analysis of Surface Electromyography

Males and females respond differently at the muscular level to various tastes and show varied responses when eating different foods. In this study, we used surface electromyography (sEMG) as a novel approach to examine gender differences in taste sensations. We collected sEMG data from 30 participants (15 males, 15 females) over various sessions for six taste states: a no-stimulation physiological state, sweet, sour, salty, bitter, and umami. We applied a Fast Fourier Transformation to the sEMG-filtered data and used a two-sample t-test algorithm to analyze and evaluate the resulting frequency spectrum. Our results showed that the female participants had more sEMG channels with low frequencies and fewer channels with high frequencies than the male participants during all taste states except the bitter taste sensation, meaning that for most sensations, the female participants had better tactile and fewer gustatory responses than the male participants. The female participants responded better to gustatory and tactile perceptions during bitter tasting because they had more channels throughout the frequency distribution. Moreover, the facial muscles of the female participants twitched with low frequencies, while the facial muscles of the male participants twitched with high frequencies for all taste states except the bitter sensation, for which the female facial muscles twitched throughout the range of the frequency distribution. This gender-dependent variation in sEMG frequency distribution provides new evidence of differentiated taste sensations between males and females.

Exploring high-density corticomuscular networks after stroke to enable a hybrid Brain-Computer Interface for hand motor rehabilitation

BackgroundBrain-Computer Interfaces (BCI) promote upper limb recovery in stroke patients reinforcing motor related brain activity (from electroencephalogaphy, EEG). Hybrid BCIs which include peripheral signals (electromyography, EMG) as control features could be employed to monitor post-stroke motor abnormalities. To ground the use of corticomuscular coherence (CMC) as a hybrid feature for a rehabilitative BCI, we analyzed high-density CMC networks (derived from multiple EEG and EMG channels) and their relation with upper limb motor deficit by comparing data from stroke patients with healthy participants during simple hand tasks.MethodsEEG (61 sensors) and EMG (8 muscles per arm) were simultaneously recorded from 12 stroke (EXP) and 12 healthy participants (CTRL) during simple hand movements performed with right/left (CTRL) and unaffected/affected hand (EXP, UH/AH). CMC networks were estimated for each movement and their properties were analyzed by means of indices derived ad-hoc from graph theory and compared among groups.ResultsBetween-group analysis showed that CMC weight of the whole brain network was significantly reduced in patients during AH movements. The network density was increased especially for those connections entailing bilateral non-target muscles. Such reduced muscle-specificity observed in patients was confirmed by muscle degree index (connections per muscle) which indicated a connections’ distribution among non-target and contralateral muscles and revealed a higher involvement of proximal muscles in patients. CMC network properties correlated with upper-limb motor impairment as assessed by Fugl-Meyer Assessment and Manual Muscle Test in patients.ConclusionsHigh-density CMC networks can capture motor abnormalities in stroke patients during simple hand movements. Correlations with upper limb motor impairment support their use in a BCI-based rehabilitative approach.

Automatic Detection of Outliers in Multi-Channel EMG Signals Using MFCC and SVM

The automatic detection of noisy channels in surface Electromyogram (sEMG) signals, at the time of recording, is very critical in making a noise-free EMG dataset. If an EMG signal contaminated by high-level noise is recorded, then it will be useless and can’t be used for any healthcare application. In this research work, a new machine learning-based paradigm is proposed to automate the detection of low-level and high-level noises occurring in different channels of high density and multi-channel sEMG signals. A modified version of mel frequency cepstral coefficients (mMFCC) is proposed for the extraction of features from sEMG channels along with other statistical parameters i-e complexity coefficient, hurst exponent, and root mean square. Several state-of-the-art classifiers such as Support Vector Machine (SVM), Ensemble Bagged Trees, Ensemble Subspace Discriminant, and Logistic Regression are used to automatically identify an EMG channel either bad or good based on these extracted features. Comparison-based analyses of these classifiers have also been considered based on total classification accuracy, prediction speed (observations/sec), and processing time. The proposed method is tested on 320 simulated EMG channels as well as 640 experimental EMG channels. SVM is used as our main classifier for the detection of noisy channels which gives a total classification accuracy of 99.4% for simulated EMG channels whereas accuracy of 98.9% is achieved for experimental EMG channels.

MUNIX repeatability evaluation method based on FastICA demixing.

To enhance the reproducibility of motor unit number index (MUNIX) for evaluating neurological disease progression, this paper proposes a negative entropy-based fast independent component analysis (FastICA) demixing method to assess MUNIX reproducibility in the presence of inter-channel mixing of electromyography (EMG) signals acquired by high-density electrodes. First, composite surface EMG (sEMG) signals were obtained using high-density surface electrodes. Second, the FastICA algorithm based on negative entropy was employed to determine the orthogonal projection matrix that minimizes the negative entropy of the projected signal and effectively separates mixed sEMG signals. Finally, the proposed experimental approach was validated by introducing an interrelationship criterion to quantify independence between adjacent channel EMG signals, measuring MUNIX repeatability using coefficient of variation (CV), and determining motor unit number and size through MUNIX. Results analysis shows that the inclusion of the full (128) channel sEMG information leads to a reduction in CV value by $1.5 \pm 0.1$ and a linear decline in CV value with an increase in the number of channels. The correlation between adjacent channels in participants decreases by $0.12 \pm 0.05$ as the number of channels gradually increases. The results demonstrate a significant reduction in the number of interrelationships between sEMG signals following negative entropy-based FastICA processing, compared to the mixed sEMG signals. Moreover, this decrease in interrelationships becomes more pronounced with an increasing number of channels. Additionally, the CV of MUNIX gradually decreases with an increase in the number of channels, thereby optimizing the issue of abnormal MUNIX repeatability patterns and further enhancing the reproducibility of MUNIX based on high-density surface EMG signals.

Human–robot interface based on sEMG envelope signal for the collaborative wearable robot

Human–robot interface based on sEMG envelope signal for the collaborative wearable robot

Low-Cost Wearable Band Sensors of Surface Electromyography for Detecting Hand Movements.

Surface electromyography (sEMG) is a non-invasive measure of electrical activity generated due to muscle contraction. In recent years, sEMG signals have been increasingly used in diverse applications such as rehabilitation, pattern recognition, and control of orthotic and prosthetic systems. This study presents the development of a versatile multi-channel sEMG low-cost wearable band system to acquire 4 signals. In this case, the signals acquired with the proposed device have been used to detect hand movements. However, the WyoFlex band could be used in some sections of the arm or the leg if the section’s diameter matches the diameter of the WyoFlex band. The designed WyoFlex band was fabricated using three-dimensional (3D) printing techniques employing thermoplastic polyurethane and polylactic acid as manufacturing materials. Then, the proposed wearable electromyographic system (WES) consists of 2 WyoFlex bands, which simultaneously allow the wireless acquisition of 4 sEMG channels of each forearm. The collected sEMG can be visualized and stored for future post-processing stages using a graphical user interface designed in Node-RED. Several experimental tests were conducted to verify the performance of the WES. A dataset with sEMG collected from 15 healthy humans has been obtained as part of the presented results. In addition, a classification algorithm based on artificial neural networks has been implemented to validate the usability of the collected sEMG signals.

Bagged tree ensemble modelling with feature selection for isometric EMG-based force estimation

EMG-based force estimation is crucial in applications, such as control of powered prosthetic and rehabilitation devices. Most previous studies focus on intra-subject force modelling. However, a generalized EMG-force estimation model, which is capable of estimating force across users, is needed for surgical and rehabilitation robotics. In this study, EMG signals are recorded from the long head and short head of the biceps brachii, and brachioradialis using 3 linear surface electrode arrays, during isometric elbow flexions, at different joint angles and forearm postures, while recording the induced force at the wrist. The recorded EMGs are pre-processed and segmented, and 336 time and frequency domain features are extracted from 21 EMG channels. We explore developing a model that can perform well across subjects, where Bagged Tree Ensemble (BTE) models are used to learn the non-linear, complex relationships between the EMG and force data. The BTE models are compared with several machine learning approaches. The BTE model performs best, giving an average normalized mean squared error (%NMSE) of 5.65±16.24%. To reduce the dimensionality of the feature space and improve force estimation performance, a novel feature selection technique, called modified sequential feature selection (MSFS) is implemented and compared to other commonly used feature selection methods. Results show that the MSFS algorithm outperformed other methods tested, significantly reducing the force estimation error. We also found that there was no effect of forearm posture and joint angle on force modelling accuracy, permitting the development of an isometric force estimation model generalized across participants and arm positions.

Assessment of submentalis muscle activity for sleep-wake classification of healthy individuals and patients with sleep disorders.

This work proposes a method utilizing only the submentalis EMG channel for the classification of sleep and wake states among the healthy individuals and patients with various sleep disorders such as sleep apnea hypopnea syndrome, dyssomnia, etc. We extracted autoregressive model parameters, discrete wavelet transform coefficients, Hjorth's complexity and mobility, relative bandpowers, Poincaré plot descriptors and statistical features from the EMG signal. We also used the energy of each epoch as a feature to distinguish between the sleep and wake states. Mutual information based feature selection approach was considered to obtain the top 25 features which provided maximum accuracy. For classification, we employed an ensemble of decision trees with random undersampling and boosting technique to deal with the class-imbalance problem in the sleep data. We achieved an overall accuracy of about 85% for the healthy population and about 70% on an average across different pathological groups. This work shows the potential of EMG chin activity for sleep analysis. Clinical Relevance- Automatic and reliable sleep-wake classification can reduce the burden of sleep experts in analyzing overnight sleep data (~ 8 hours) and also assist them to diagnose various neurological disorders at an early stage. Utilizing EMG channel provides an easier and convenient long-term recording of data without causing much disturbance in sleepunlike EEG which is inconvenient and hampers the natural sleep.

Sparse representation of brain signals offers effective computation of cortico-muscular coupling value to predict the task-related and non-task sEMG channels: A joint hdEEG-sEMG study.

Cortico-muscular interactions play important role in sensorimotor control during motor task and are commonly studied by cortico-muscular coherence (CMC) method using joint electroencephalogram-surface electromyogram (EEG-sEMG) signals. As noise and time delay between the two signals weaken the CMC value, coupling difference between non-task sEMG channels is often undetectable. We used sparse representation of EEG channels to compute CMC and detect coupling for task-related and non-task sEMG signals. High-density joint EEG-sEMG (53 EEG channels, 4 sEMG bipolar channels) signals were acquired from 15 subjects (30.26 ± 4.96 years) during four specific hand and foot contraction tasks (2 dynamic and 2 static contraction). Sparse representations method was applied to detect projection of EEG signals on each sEMG channel. Bayesian optimization was employed to select best-fitted method with tuned hyperparameters on the input feeding data while using 80% data as the train set and 20% as test set. K-fold (K = 5) cross-validation method was used for evaluation of trained model. Two models were trained separately, one for CMC data and the other from sparse representation of EEG channels on each sEMG channel. Sensitivity, specificity, and accuracy criteria were obtained for test dataset to evaluate the performance of task-related and non-task sEMG channels detection. Coupling values were significantly different between grand average of task-related compared to the non-task sEMG channels (Z = -6.33, p< 0.001, task-related median = 2.011, non-task median = 0.112). Strong coupling index was found even in single trial analysis. Sparse representation approach (best fitted model: SVM, Accuracy = 88.12%, Sensitivity = 83.85%, Specificity = 92.45%) outperformed CMC method (best fitted model: KNN, Accuracy = 50.83%, Sensitivity = 52.17%, Specificity = 49.47%). Sparse representation approach offers high performance to detect CMC for discerning the EMG channels involved in the contraction tasks and non-tasks.