Effect of threshold values on the combination of EMG time domain features: Surface versus intramuscular EMG

ExoMechHand prototype development and testing with EMG signals for hand rehabilitation

Rehabilitation is a major requirement to improve the quality of life and mobility of patients with disabilities. The use of rehabilitative devices without continuous supervision of medical experts is increasing manifold, mainly due to prolonged therapy costs and advancements in robotics. Due to ExoMechHand's inexpensive cost, high robustness, and efficacy for participants with median and ulnar neuropathies, we have recommended it as a rehabilitation tool in this study. ExoMechHand is coupled with three different resistive plates for hand impairment. For efficacy, ten unhealthy subjects with median or ulnar nerve neuropathies are considered. After twenty days of continuous exercise, three subjects showed improvement in their hand grip, range of motion of the wrist, or range of motion of metacarpophalangeal joints. The condition of the hand is assessed by features of surface-electromyography signals. A Machine-learning model based on these features of fifteen subjects is used for staging the condition of the hand. Machine-learning algorithms are trained to indicate the type of resistive plate to be used by the subject without the need for examination by the therapist. The extra-trees classifier came out to be the most effective algorithm with 98% accuracy on test data for indicating the type of resistive plate, followed by random-forest and gradient-boosting with accuracies of 95% and 93%, respectively. Results showed that the staging of hand condition could be analyzed by sEMG signal obtained from the flexor-carpi-ulnaris and flexor-carpi-radialis muscles in subjects with median and ulnar neuropathies.

Optimizing the performance of convolutional neural network for enhanced gesture recognition using sEMG

Deep neural networks (DNNs) have demonstrated higher performance results when compared to traditional approaches for implementing robust myoelectric control (MEC) systems. However, the delay induced by optimising a MEC remains a concern for real-time applications. As a result, an optimised DNN architecture based on fine-tuned hyperparameters is required. This study investigates the optimal configuration of convolutional neural network (CNN)-based MEC by proposing an effective data segmentation technique and a generalised set of hyperparameters. Firstly, two segmentation strategies (disjoint and overlap) and various segment and overlap sizes were studied to optimise segmentation parameters. Secondly, to address the challenge of optimising the hyperparameters of a DNN-based MEC system, the problem has been abstracted as an optimisation problem, and Bayesian optimisation has been used to solve it. From 20 healthy people, ten surface electromyography (sEMG) grasping movements abstracted from daily life were chosen as the target gesture set. With an ideal segment size of 200 ms and an overlap size of 80%, the results show that the overlap segmentation technique outperforms the disjoint segmentation technique (p-value < 0.05). In comparison to manual (12.76 ± 4.66), grid (0.10 ± 0.03), and random (0.12 ± 0.05) search hyperparameters optimisation strategies, the proposed optimisation technique resulted in a mean classification error rate (CER) of 0.08 ± 0.03 across all subjects. In addition, a generalised CNN architecture with an optimal set of hyperparameters is proposed. When tested separately on all individuals, the single generalised CNN architecture produced an overall CER of 0.09 ± 0.03. This study's significance lies in its contribution to the field of EMG signal processing by demonstrating the superiority of the overlap segmentation technique, optimizing CNN hyperparameters through Bayesian optimization, and offering practical insights for improving prosthetic control and human-computer interfaces.

Evaluation of skin sympathetic nervous activity for classification of intracerebral hemorrhage and outcome prediction

Classification and outcome prediction of intracerebral hemorrhage (ICH) is critical for improving the survival rate of patients. Early or delayed neurological deterioration is common in ICH patients, which may lead to changes in the autonomic nervous system (ANS). Therefore, we proposed a new framework for ICH classification and outcome prediction based on skin sympathetic nervous activity (SKNA) signals. A customized measurement device presented in our previous papers was used to collect data. 117 subjects (50 healthy control subjects and 67 ICH patients) were recruited for this study to obtain their 5-min electrocardiogram (ECG) and SKNA signals. We extracted the signal's time-domain, frequency-domain, and nonlinear features and analyzed their differences between healthy control subjects and ICH patients. Subsequently, we established the ICH classification and outcome evaluation model based on the eXtreme Gradient Boosting (XGBoost). In addition, heart rate variability (HRV) as an ANS assessment method was also included as a comparison method in this study. The results showed significant differences in most features of the SKNA signal between healthy control subjects and ICH patients. The ICH patients with good outcomes have a higher change rate and complexity of SKNA signal than those with bad outcomes. In addition, the accuracy of the model for ICH classification and outcome prediction based on the SKNA signal was more than 91% and 83%, respectively. The ICH classification and outcome prediction based on the SKNA signal proved to be a feasible method in this study. Furthermore, the features of change rate and complexity, such as entropy measures, can be used to characterize the difference in SKNA signals of different groups. The method can potentially provide a new tool for rapid classification and outcome prediction of ICH patients. Index Terms-intracerebral hemorrhage (ICH), skin sympathetic nervous activity (SKNA), classification, outcome prediction, cardiovascular and cerebrovascular diseases.

Wearable Electromyography Classification of Epileptic Seizures: A Feasibility Study

Accurate diagnosis and classification of epileptic seizures can greatly support patient treatments. As many epileptic seizures are convulsive and have a motor component, the analysis of muscle activity can provide valuable information for seizure classification. Therefore, this paper present a feasibility study conducted on healthy volunteers, focusing on tracking epileptic seizures movements using surface electromyography signals (sEMG) measured on human limb muscles. For the experimental studies, first, compact wireless sensor nodes were developed for real-time measurement of sEMG on the gastrocnemius, flexor carpi ulnaris, biceps brachii, and quadriceps muscles on the right side and the left side. For the classification of the seizure, a machine learning model has been elaborated. The 16 common sEMG time-domain features were first extracted and examined with respect to discrimination and redundancy. This allowed the features to be classified into irrelevant features, important features, and redundant features. Redundant features were examined with the Big-O notation method and with the average execution time method to select the feature that leads to lower complexity and reduced processing time. The finally selected six features were explored using different machine learning classifiers to compare the resulting classification accuracy. The results show that the artificial neural network (ANN) model with the six features: IEMG, WAMP, MYOP, SE, SKEW, and WL, had the highest classification accuracy (99.95%). A further study confirms that all the chosen eight sensors are necessary to reach this high classification accuracy.

Surface EMG Statistical and Performance Analysis of Targeted-Muscle-Reinnervated (TMR) Transhumeral Prosthesis Users in Home and Laboratory Settings

A pattern-recognition (PR)-based myoelectric control system is the trend of future prostheses development. Compared with conventional prosthetic control systems, PR-based control systems provide high dexterity, with many studies achieving >95% accuracy in the last two decades. However, most research studies have been conducted in the laboratory. There is limited research investigating how EMG signals are acquired when users operate PR-based systems in their home and community environments. This study compares the statistical properties of surface electromyography (sEMG) signals used to calibrate prostheses and quantifies the quality of calibration sEMG data through separability indices, repeatability indices, and correlation coefficients in home and laboratory settings. The results demonstrate no significant differences in classification performance between home and laboratory environments in within-calibration classification error (home: 6.33 ± 2.13%, laboratory: 7.57 ± 3.44%). However, between-calibration classification errors (home: 40.61 ± 9.19%, laboratory: 44.98 ± 12.15%) were statistically different. Furthermore, the difference in all statistical properties of sEMG signals is significant (p < 0.05). Separability indices reveal that motion classes are more diverse in the home setting. In summary, differences in sEMG signals generated between home and laboratory only affect between-calibration performance.

Biosignal processing methods to explore the effects of side-dominance on patterns of bi- and unilateral standing stability in healthy young adults

We examined the effects of side-dominance on the laterality of standing stability using ground reaction force, motion capture (MoCap), and EMG data in healthy young adults. We recruited participants with strong right (n = 15) and left (n = 9) hand and leg dominance (side-dominance). They stood on one or two legs on a pair of synchronized force platforms for 50 s with 60 s rest between three randomized stance trials. In addition to 23 CoP-related variables, we also computed six MoCap variables representing each lower-limb joint motion time series. Moreover, 39 time- and frequency-domain features of EMG data from five muscles in three muscle groups were analyzed. Data from the multitude of biosignals converged and revealed concordant patterns: no differences occurred between left- and right-side dominant participants in kinetic, kinematic, or EMG outcomes during bipedal stance. Regarding single leg stance, larger knee but lower ankle joint kinematic values appeared in left vs right-sided participants during non-dominant stance. Left-vs right-sided participants also had lower medial gastrocnemius EMG activation during non-dominant stance. While right-side dominant participants always produced larger values for kinematic data of ankle joint and medial gastrocnemius EMG activation during non-dominant vs dominant unilateral stance, this pattern was the opposite for left-sided participants, showing larger values when standing on their dominant vs non-dominant leg, i.e., participants had a more stable balance when standing on their right leg. Our results suggest that side-dominance affects biomechanical and neuromuscular control strategies during unilateral standing.

An intelligent neuromarketing system for predicting consumers' future choice from electroencephalography signals

Neuromarketing utilizes Brain-Computer Interface (BCI) technologies to provide insight into consumers responses on marketing stimuli. In order to achieve insight information, marketers spend about $400 billion annually on marketing, promotion, and advertisement using traditional marketing research tools. In addition, these tools like personal depth interviews, surveys, focus group discussions, etc. are expensive and frequently criticized for failing to extract actual consumer preferences. Neuromarketing, on the other hand, promises to overcome such constraints. In this work, an EEG-based neuromarketing framework is employed for predicting consumer future choice (affective attitude) while they view E-commerce products. After preprocessing, three types of features, namely, time, frequency, and time-frequency domain features are extracted. Then, wrapper-based Support Vector Machine-Recursive Feature Elimination (SVM-RFE) along with correlation bias reduction is used for feature selection. Lastly, we use SVM for categorizing positive affective attitude and negative affective attitude. Experiments show that the frontal cortex achieves the best accuracy of 98.67±2.98, 98±3.22, and 98.67±3.52 for 5-fold, 10-fold, and leave-one-subject-out (LOSO) respectively. In addition, among all the channels, F_z achieves best accuracy 90±7.81, 90.67±9.53, and 92.67±7.03 for 5-fold, 10-fold, and LOSO respectively. Subsequently, this work opens the door for implementing such a neuromarketing framework using consumer-grade devices in a real-life setting for marketers. As a result, it is evident that EEG-based neuromarketing technologies can assist brands and enterprises in forecasting future consumer preferences accurately. Hence, it will pave the way for the creation of an intelligent marketing assistive system for neuromarketing applications in future.

Scalable tensor factorization for recovering multiday missing intramuscular electromyography data

To design a prosthetic hand which can classify movements based on the electromyography (EMG) signals, complete and good quality signals are essential. However, due to different reasons such as disconnection of electrodes or muscles fatigue the recorded EMG data can be incomplete, which degrades the classification of test movements. In this paper, we first acquire multiday intramuscular EMG (iEMG) signals (which are invasive) with higher Signal-to-Noise Ratio (SNR) compared to surface EMG (sEMG) signals; followed by application of matrix (non-negative matrix factorization – NMF) and tensor factorization methods (Canonical Polyadic Decomposition (CPD), Tucker decomposition (TD) & Canonical Polyadic-Weighted Optimization (CP-WOPT)) for recovering structured missing data i.e., chunks of missing samples in channels. Furthermore, we tested the scalability of NMF, CPD, TD and CP-WOPT by employing them on the large multiday (seven days) iEMG data where the size of missing data is increased from day 1 to day 7, and for each day a fixed percentage of missing data is introduced from 10% to worst case of 50%. Results show that CP-WOPT outperformed NMF, CPD and TD to recover large percentage of missing data in terms of Relative Mean Error (RME) even when 7 days of data is considered. CP-WOPT showed robustness even for the worse case even when 50% iEMG data is removed from day 1 to day 7 where it’s RME degraded slightly from 0.08 to 0.1.

BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework

Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how customers react to marketing stimuli. Marketers spend about $750 billion annually on traditional marketing camping. They use traditional marketing research procedures such as Personal Depth Interviews, Surveys, Focused Group Discussions, and so on, which are frequently criticized for failing to extract true consumer preferences. On the other hand, Neuromarketing promises to overcome such constraints. This work proposes a machine learning framework for predicting consumers' purchase intention (PI) and affective attitude (AA) from analyzing EEG signals. In this work, EEG signals are collected from 20 healthy participants while administering three advertising stimuli settings: product, endorsement, and promotion. After preprocessing, features are extracted in three domains (time, frequency, and time-frequency). Then, after selecting features using wrapper-based methods Recursive Feature Elimination, Support Vector Machine is used for categorizing positive and negative (AA and PI). The experimental results show that proposed framework achieves an accuracy of 84 and 87.00% for PI and AA ensuring the simulation of real-life results. In addition, AA and PI signals show N200 and N400 components when people tend to take decision after visualizing static advertisement. Moreover, negative AA signals shows more dispersion than positive AA signals. Furthermore, this work paves the way for implementing such a neuromarketing framework using consumer-grade EEG devices in a real-life setting. Therefore, it is evident that BCI-based neuromarketing technology can help brands and businesses effectively predict future consumer preferences. Hence, EEG-based neuromarketing technologies can assist brands and enterprizes in accurately forecasting future consumer preferences.

Review on electromyography based intention for upper limb control using pattern recognition for human-machine interaction

Upper limb myoelectric prosthetic control is an essential topic in the field of rehabilitation. The technique controls prostheses using surface electromyogram (sEMG) and intramuscular EMG (iEMG) signals. EMG signals are extensively used in controlling prosthetic upper and lower limbs, virtual reality entertainment, and human-machine interface (HMI). EMG signals are vital parameters for machine learning and deep learning algorithms and help to give an insight into the human brain’s function and mechanisms. Pattern recognition techniques pertaining to support vector machine (SVM), k-nearest neighbor (KNN) and Bayesian classifiers have been utilized to classify EMG signals. This paper presents a review on current EMG signal techniques, including electrode array utilization, signal acquisition, signal preprocessing and post-processing, feature selection and extraction, data dimensionality reduction, classification, and ultimate application to the community. The paper also discusses using alternatives to EMG signals, such as force sensors, to measure muscle activity with reliable results. Future implications for EMG classification include employing deep learning techniques such as artificial neural networks (ANN) and recurrent neural networks (RNN) for achieving robust results.

Muscle-Specific High-Density Electromyography Arrays for Hand Gesture Classification

OBJECTIVE

Dexterous hand motion is critical for object manipulation. Electrophysiological studies of the hand are key to understanding its underlying mechanisms. High-density electromyography (HD-EMG) provides spatio-temporal information about the underlying electrical activity of muscles, which can be used in neurophysiological research, rehabilitation and control applications. However, existing EMG electrodes platforms are not muscle-specific, which makes the assessment of intrinsic hand muscles difficult.

METHODS

Muscle-specific flexible HD-EMG electrode arrays were developed to capture intrinsic hand muscle myoelectric activity during manipulation tasks. The arrays consist of 60 individual electrodes targeting 10 intrinsic hand muscles. Myoelectric activity was displayed as spatio-temporal amplitude maps to visualize muscle activation. Time-domain and temporal-spatial HD-EMG features were extracted to train cubic support vector machine machine-learning classifiers to classify the intended user motion.

RESULTS

Experimental data was collected from 5 subjects performing a range of 10 common hand motions. Spatio-temporal EMG maps showed distinct activation areas correlated to the muscles recruited during each movement. The thenar muscle fiber conduction velocity (CV) was estimated to be at 4.70.3 m/s for all subjects. Hand motions were successfully classified and average accuracy for all subjects was directly related to spatial resolution based on the number of channels used as inputs; ranging from 744% when using only 5 channels and up to 922% when using 41 channels. Temporal-spatial features were shown to provide increased motion-specific accuracy when similar muscles were recruited for different gestures.

CONCLUSIONS

Muscle-specific electrodes were capable of accurately recording HD-EMG signals from intrinsic hand muscles and accurately predicting motion.

SIGNIFICANCE

The muscle-specific electrode arrays could improve electrophysiological research studies using EMG decomposition techniques to assess motor unit activity and in applications involving the analysis of dexterous hand motions.

Inter-classifier comparison for upper extremity EMG signal at different hand postures and arm positions using pattern recognition

The utilization of surface EMG and intramuscular EMG signals has been observed to create significant improvement in pattern recognition approaches and myoelectric control. However, there is less data of different arm positions and hand postures available. Hand postures and arm positions tend to affect the combination of surface and intramuscular EMG signal acquisition in terms of classifier accuracy. Hence, this study aimed to find a robust classifier for two scenarios: (1) at fixed arm position (FAP) where classifiers classify different hand postures and (2) at fixed hand posture (FHP) where classifiers classify different arm positions. A total of 20 healthy male participants (30.62 ± 3.87 years old) were recruited for this study. They were asked to perform five motion classes including hand grasp, hand open, rest, hand extension, and hand flexion at four different arm positions at 0°, 45°, 90°, and 135°. SVM, KNN, and LDA classifier were deployed. Statistical analysis in the form of pairwise comparisons was carried out using SPSS. It is concluded that there is no significant difference among the three classifiers. SVM gave highest accuracy of 75.35% and 58.32% at FAP and FHP respectively for each motion classification. KNN yielded the highest accuracies of 69.11% and 79.04% when data was pooled and was classified at different arm positions and at different hand postures respectively. The results exhibited that there is no significant effect of changing arm position and hand posture on the classifier accuracy.

Evaluation of windowing techniques for intramuscular EMG-based diagnostic, rehabilitative and assistive devices

Objective.Intramuscular electromyography (iEMG) signals, invasively recorded, directly from the muscles are used to diagnose various neuromuscular disorders/diseases and to control rehabilitative and assistive robotic devices. iEMG signals are potentially being used in neurology, kinesiology, rehabilitation and ergonomics, to detect/diagnose various diseases/disorders. Electromyography-based classification and analysis systems are being designed and tested for the classification of various neuromuscular disorders and to control rehabilitative and assistive robotic devices. Many studies have explored parameters such as the pre-processing, feature extraction and selection of classifiers that can affect the performance and efficacy of iEMG-based classification systems. The pre-processing stage includes the removal of any unwanted noise from the original signal and windowing of the signal.Approach.This study investigated and presented the optimum windowing configurations for robust control and better performance results of an iEMG-based analysis system based on the stationarity rate (SR) and classification accuracy. Both disjoint and overlap, windowing techniques with varying window and overlap sizes have been investigated using a machine learning-based classification algorithm called linear discriminant analysis.Main results.The optimum window size ranges are from 200-300 ms for the disjoint and 225-300 ms for the overlap windowing technique, respectively. The inferred results show that for the overlap windowing technique the optimum range of overlap size is from 10%-30% of the length of window size. The mean classification accuracy (MCA) and mean stationarity rate (MSR) were found to be lower in the disjoint windowing technique compared to overlap windowing at all investigated overlap sizes. Statistical analysis (two-way analysis of variance test) showed that the MSR and MCA of the overlap windowing technique was significantly different at overlap sizes of 10%-30% (p-values < 0.05).Significance.The presented results can be used to achieve the best possible classification results and SR for any iEMG-based real-time diagnosis, detection and control system, which can enhance the performance of the system significantly.

Exploration of Human Activity Recognition Using a Single Sensor for Stroke Survivors and Able-Bodied People

Commonly used sensors like accelerometers, gyroscopes, surface electromyography sensors, etc., which provide a convenient and practical solution for human activity recognition (HAR), have gained extensive attention. However, which kind of sensor can provide adequate information in achieving a satisfactory performance, or whether the position of a single sensor would play a significant effect on the performance in HAR are sparsely studied. In this paper, a comparative study to fully investigate the performance of the aforementioned sensors for classifying four activities (walking, tooth brushing, face washing, drinking) is explored. Sensors are spatially distributed over the human body, and subjects are categorized into three groups (able-bodied people, stroke survivors, and the union of both). Performances of using accelerometer, gyroscope, sEMG, and their combination in each group are evaluated by adopting the Support Vector Machine classifier with the Leave-One-Subject-Out Cross-Validation technique, and the optimal sensor position for each kind of sensor is presented based on the accuracy. Experimental results show that using the accelerometer could obtain the best performance in each group. The highest accuracy of HAR involving stroke survivors was 95.84 ± 1.75% (mean ± standard error), achieved by the accelerometer attached to the extensor carpi ulnaris. Furthermore, taking the practical application of HAR into consideration, a novel approach to distinguish various activities of stroke survivors based on a pre-trained HAR model built on healthy subjects is proposed, the highest accuracy of which is 77.89 ± 4.81% (mean ± standard error) with the accelerometer attached to the extensor carpi ulnaris.

Hybrid Impedance-Admittance Control for Upper Limb Exoskeleton Using Electromyography

Exoskeletons are wearable mobile robots that combine various technologies to enable limb movement with greater strength and endurance, being used in several application areas, such as industry and medicine. In this context, this paper presents the development of a hybrid control method for exoskeletons, combining admission and impedance control based on electromyographic input signals. A proof of concept of a robotic arm with two degrees of freedom, mimicking the functions of a human’s upper limb, was built to evaluate the proposed control system. Through tests that measured the discrepancy between the angles of the human joint and the joint of the exoskeleton, it was possible to determine that the system remained within an acceptable error range. The average error is lower than 4.3%, and the robotic arm manages to mimic the movements of the upper limbs of a human in real-time.

A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN

Recent developments in implantable technology, such as high-density recordings, wireless transmission of signals to a prosthetic hand, may pave the way for intramuscular electromyography (iEMG)-based myoelectric control in the future. This study aimed to investigate the real-time control performance of iEMG over time. A novel protocol was developed to quantify the robustness of the real-time performance parameters. Intramuscular wires were used to record EMG signals, which were kept inside the muscles for five consecutive days. Tests were performed on multiple days using Fitts' law. Throughput, completion rate, path efficiency and overshoot were evaluated as performance metrics using three train/test strategies. Each train/test scheme was categorized on the basis of data quantity and the time difference between training and testing data. An artificial neural network (ANN) classifier was trained and tested on (i) data from the same day (WDT), (ii) data collected from the previous day and tested on present-day (BDT) and (iii) trained on all previous days including the present day and tested on present-day (CDT). It was found that the completion rate (91.6 ± 3.6%) of CDT was significantly better (p < 0.01) than BDT (74.02 ± 5.8%) and WDT (88.16 ± 3.6%). For BDT, on average, the first session of each day was significantly better (p < 0.01) than the second and third sessions for completion rate (77.9 ± 14.0%) and path efficiency (88.9 ± 16.9%). Subjects demonstrated the ability to achieve targets successfully with wire electrodes. Results also suggest that time variations in the iEMG signal can be catered by concatenating the data over several days. This scheme can be helpful in attaining stable and robust performance.