Single-channel Electromyography Research Articles

Simple recognition of hand gestures using single-channel EMG signals.

Electromyography (EMG) signals are used for many different purposes, such as recording and measuring the electrical activity generated by varying the body's skeletal muscles. Biosignals are different types of biomedical signals, like EMG signals, which can be used for the neural linkage with computers and are obtained from a particular part of the body such as tissue, muscle, organ, or cell system like the nervous system. Surface electromyography (SEMG) is a non-invasive method that can be used as an effective system for controlling upper arm prostheses. This study focused on classifying the five types of distinct finger movements investigated in four unique channels.We have used a classification technique, the k-nearest neighbors (KNN), to categorize the collected samples. Two time-domain features, (a) maximum (Max) and (b) minimum (Min), were used with one of these three features separately: mean absolute value (MAV), root mean square (RMS), and simple square integral (SSI) to classify gestures. We chose classification accuracy as a criterion for evaluating the effectiveness of every classification. We figured out that the first grouping, that is, (MAV, Max, Min), was the best choice for classification. The accuracy percentage in the four channels for the first group was 91.0%, 89.9%, 89.8%, and 96.0%, respectively.

Implementation of an Intelligent EMG Signal Classifier Using Open-Source Hardware

This paper presents the implementation of an intelligent real-time single-channel electromyography (EMG) signal classifier based on open-source hardware. The article shows the experimental design, analysis, and implementation of a solution to identify four muscle movements from the forearm (extension, pronation, supination, and flexion), for future applications in transradial active prostheses. An EMG signal acquisition instrument was developed, with a 20–450 Hz bandwidth and 2 kHz sampling rate. The signals were stored in a Database, as a multidimensional array, using a desktop application. Numerical and graphic analysis approaches for discriminative capacity were proposed for feature analysis and four feature sets were used to feed the classifier. Artificial Neural Networks (ANN) were implemented for time-domain EMG pattern recognition (PR). The system obtained a classification accuracy of 98.44% and response times per signal of 8.522 ms. Results suggest these methods allow us to understand, intuitively, the behavior of user information.

Low Complex CORDIC-based Hand Movement Recognition Design Methodology for Rehabilitation and Prosthetic Applications.

Hand movement recognition using Electromyography (EMG) signals have gained much significance lately and is extensively used for rehabilitation and prosthetic applications including stroke-driven disability and other neuromuscular disorders. Herein, quantitative analysis of EMG signals is very crucial. However, such applications are constrained by power consumption limitations due to the battery backup necessitating low-complex system design and the on-chip area requirement. Existing hand movement recognition methodologies using single-channel EMG signal involve computationally intensive stages, including Ensemble Empirical Mode Decomposition (EEMD), Fast Independent Component Analysis (FastICA), feature extraction, and Linear Discriminant Analysis (LDA) classification, which can not be mapped onto the low-complex architecture directly from the algorithmic level. The high computational complexity of LDA classification makes it difficult to be used for low-complex applications. In this paper, we introduce a low-complex CORDIC-based hand movement recognition design methodology targeting resource-constrained rehabilitation applications. This work explores replacing LDA classification with K-Means clustering due to its reduced complexity and efficient clustering algorithm. CORDIC-based K-Means clustering is used to further reduce the overall computational complexity of the system. The proposed low complex, K-Means clustering-based hand movement recognition for classifying seven hand movements using single-channel EMG data is found to be 99.77 % less complex and 1.28% more accurate than the conventional LDA-based classification.

Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals: A Review

Electromyography (EMG) is gaining importance in many research and clinical applications, including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical diagnosis of neuromuscular diseases and quantification of force. However, EMG signals can be contaminated by various types of noise, interference and artifacts, leading to potential data misinterpretation. Even assuming best practices, the acquired signal may still contain contaminants. The aim of this paper is to review methods employed to reduce the contamination of single channel EMG signals. Specifically, we focus on methods which enable a full reconstruction of the EMG signal without loss of information. This includes subtraction methods used in the time domain, denoising methods performed after the signal decomposition and hybrid approaches that combine multiple methods. Finally, this paper provides a discussion on the suitability of the individual methods based on the type of contaminant(s) present in the signal and the specific requirements of the application.

Prediction Method for Large Grasping Force using Single-Channel Surface Electromyography: A Pilot Study

Frequent grasping with a large force in the workplace leads to work-related musculoskeletal disorders (WMSDs). Thus, the frequency and force of grasping in each worker should be monitored for preventing these WMSDs. The frequency of grasping can be counted by observation and existing methods using vision-based devices or wearable sensors. Grasping force measurement generally requires force sensors between the hand and objects. Almost force sensors are limited to the surface and shape of the object in measurement; thus, measurement method without force sensors is necessary to monitor grasping force in various workplaces. Previous studies developed measurement methods for grasping force using electromyography (EMG). However, almost existing methods require multiple-channels EMG for grasping force measurement. In addition, many methods were not applied to large grasping forces. Therefore, the objective of this study is to develop the prediction method for large grasping force using single-channel EMG. The proposed method predicts grasping force by machine learning and features calculated from single channel surface EMG (sEMG) signal on flexor digitorum superficialis (FDS) muscle. As a pilot study, the proposed method was tested by sEMG data of different large grasping forces (5 to 60 kgf). The results showed that the correlation between the actual and predicted grasping force was greater than 0.8. These results indicate that the proposed method could measure large grasping force by only single-channel sEMG.

Electromyography Signal Analysis and Classification using Time-Frequency Representations and Deep Learning.

Analysis and classification of electromyography (EMG) signals are crucial for rehabilitation and motor control. This study investigates electromyogram (EMG) time-frequency representations and then creates conventional and deep learning models for EMG signal classification. Firstly, a dataset of single-channel surface EMG signals has been recorded for four subjects to differentiate between forearm flexion and extension. Then, different time-frequency EMG representations have been used to build conventional and deep learning models for EMG classification. We compared the performance of pre-trained convolutional neural network models, namely GoogLeNet, SqueezeNet and AlexNet, and achieved accuracies of 92.71%, 90.63% and 87.5%, respectively. Also, data augmentation techniques on the levels of raw EMG signals and their time- frequency representations helped improve the accuracy of GoogLeNet to 96.88%. Furthermore, our approach demonstrated superior performance on another publicly available 10-class EMG dataset, and also using traditional classifiers trained on hand-crafted features.

Comparative Analysis of Intellectual Methods for Muscular Contraction Interpretation for Gesture Interface Implementation

The paper considers comparative analysis results of the machine learning methods used for the gesture recognition based on the surface single-channel electromyography (sEMG) data. The data were processed using multilayer perceptron, support vector machine, decision tree ensemble (Random Forest) and logistic regression for the chosen four gesture types. The conclusion was derived on the analysis efficiency of these methods using commonly recommended accuracy metrics.

Daytime masticatory muscle electromyography biofeedback regulates the phasic component of sleep bruxism.

Electromyography (EMG) biofeedback (BF) training is potentially an effective cognitive-behavioural approach to regulate bruxism. This study examined sleep bruxism regulation by daytime clenching control using a single-channel auditory EMG BF device. Seventeen male subjects (mean age, 24.4±3.1years; mean±SD) with self-reported awake/sleep bruxism were recruited and divided into a BF (n=10) and a control (CO) group (n=7). All subjects underwent four EMG recording sessions during both daytime and sleep over 3weeks. During the daytime, in week 2, the BF group received feedback alert signals when excessive EMG activity with certain burst duration was detected while the subjects performed regular daily activities. The CO group underwent EMG recording sessions without receiving any alerts of parafunctional activity. The number of phasic burst events during sleep was compared between the BF and CO groups. While the number of phasic EMG events was not significantly different between the BF and CO groups at baseline, significantly smaller phasic events were observed in the BF compared to the CO group at the follow-up session (week 3) (P=.006, Tukey's HSD). Since daytime BF training is aimed at raising awareness of awake bruxism, it does not interrupt the sleep sequence or involve associated side effects. The present results suggest that EMG BF targeting fortonic EMG events during the daytime can be an effective method to regulate phasic EMG events during sleep.

Algorithms for classification of a single channel EMG signal for human-computer interaction

One of the most accurate and effective ways to control gestures is to control muscle activity, which occurs with any movement. Electromyography (EMG) is used to record such activity. This article compares SVM classification algorithms, perceptron, random trees and the method of density of probability in relation to the EMG signal. Arduino Leonardo with a single-channel Shield EMG is used to record the signal. The aim of this paper is to prove the possibility of creating a cheap and accessible biointerface based on EMG signal.

Noise-assisted multivariate empirical mode decomposition for multichannel EMG signals

BackgroundEnsemble Empirical Mode Decomposition (EEMD) has been popularised for single-channel Electromyography (EMG) signal processing as it can effectively extract the temporal information of the EMG time series. However, few papers examine the temporal and spatial characteristics across multiple muscle groups in relation to multichannel EMG signals.ExperimentThe experimental data was obtained from the Center for Machine Learning and Intelligent Systems, University of California Irvine (UCI). The data was donated by the Nueva Granada Military University and the Technopark node Manizales in Colombia. The databases of 11 male subjects from the healthy group were taken into the study. The subjects undergo three exercise programs, leg extension from a sitting position (sitting), flexion of the leg up (standing), and gait (walking), while four electrodes were placed on biceps femoris (BF), vastus medialis (VM), rectus femoris (RF), and semitendinosus (ST).MethodsBased on the experimental data, a comparative study is provided by assessing the Empirical Mode Decomposition (EMD)-based approaches, EEMD, Multivariate EMD (MEMD), and Noise-Assisted MEMD (NA-MEMD). The outcomes from these approaches are then quantitatively estimated on the basis of three criterions, the number of Intrinsic Mode Functions (IMFs), mode-alignment and mode-mixing.ResultsBoth MEMD and NA-MEMD methods (except EEMD) can guarantee equal numbers of IMFs. For mode-alignment and mode-mixing, NA-MEMD is optimal compared with MEMD and EEMD, and MEMD is merely better than EEMD.ConclusionsThis study proposes the NA-MEMD approach for multichannel EMG signal processing. This finding implies that NA-MEMD is effective for simultaneously analysing IMFs based frequency bands. It has a vital clinical implication in exploring the neuromuscular patterns that enable the multiple muscle groups to coordinate while performing the functional activities of daily living.

Agreement between jaw-muscle activity measurement with portable single-channel electromyography and polysomnography in children.

Polysomnography (PSG) is the gold standard for sleep bruxism (SB) assessment. High economical costs, complex technical equipment, and unfamiliar laboratory setting limit its use in children. To determine the night-to-night variability of electromyography (EMG) episodes during a five-night recording with the GrindCare Measure (GCM), and the agreement in the assessment of masticatory muscle activity (MMA) between GCM and PSG in children. Forty-seven children from clinics of Universidad CES participated. Each participant was assessed with GCM for five consecutive nights. The last night, children underwent a single-night PSG study, together with the GCM. Spearman correlation coefficients were used to analyze data. The frequency of SB occurrence was 'sometimes' in 12 (25.5%) and 'usually' in 19 (40.4%) children. Simultaneous measurements with GCM and PSG obtained during the fifth night of measurement were not significantly correlated. Correlation between GCM total EMG episodes and EMG episodes/h and PSG total SB episodes, SB episodes/h, total bursts and burst/h measured with PSG was also not significant. EMG measurement with GCM was not accurate to detect PSG/SB in children. There was not advantage of multiple assessment for five nights with GCM, reducing the impact of night-to-night EMG episodes' variability on the GCM/PSG correlation.

Diagnostic validity of the use of a portable single-channel electromyography device for sleep bruxism.

The aim of this study was to determine an appropriate cutoff value and the number of nights of sleep with the portable single-channel EMG device (GrindCare) necessary for a valid sleep bruxism (SB) diagnosis. Twenty consecutive post-graduate students and staff at Bauru School of Dentistry composed the sample. Each participant underwent the GrindCare for five consecutive nights and the polysomnography (PSG). The discrimination between bruxers and non-bruxers was based only on the PSG analysis. Data about electromyography per hour with GrindCare (EMG/h) and PSG (bursts/h) were scored. There were positive correlations between the two devices for EMG/h and bursts/h in three and five consecutive nights. Bland-Altman analysis of the EMG bursts/h showed positive agreement between the methods. The receiver operating characteristic (ROC) analyses also showed that using a minimum of 18 EMG/h for three nights and 19 EMG/h for five nights in GrindCare as cutoffs resulted in a 90% specificity and positive likelihood ratio equal to 5. GrindCare is able to discriminate SB diagnosed by PSG and gold standard criteria, when used for three or five consecutive nights, and it may be a valid choice in clinical practice for SB assessment.

Single-Channel EMG Classification With Ensemble-Empirical-Mode-Decomposition-Based ICA for Diagnosing Neuromuscular Disorders.

An accurate and computationally efficient quantitative analysis of electromyography (EMG) signals plays an inevitable role in the diagnosis of neuromuscular disorders, prosthesis, and several related applications. Since it is often the case that the measured signals are the mixtures of electric potentials that emanate from surrounding muscles (sources), many EMG signal processing approaches rely on linear source separation techniques such as the independent component analysis (ICA). Nevertheless, naive implementations of ICA algorithms do not comply with the task of extracting the underlying sources from a single-channel EMG measurement. In this respect, the present work focuses on a classification method for neuromuscular disorders that deals with the data recorded using a single-channel EMG sensor. The ensemble empirical mode decomposition algorithm decomposes the single-channel EMG signal into a set of noise-canceled intrinsic mode functions, which in turn are separated by the FastICA algorithm. A reduced set of five time domain features extracted from the separated components are classified using the linear discriminant analysis, and the classification results are fine-tuned with a majority voting scheme. The performance of the proposed method has been validated with a clinical EMG database, which reports a higher classification accuracy (98%). The outcome of this study encourages possible extension of this approach to real settings to assist the clinicians in making correct diagnosis of neuromuscular disorders.

Fractal analysis features for weak and single-channel upper-limb EMG signals

Electromyography (EMG) signals are the electrical manifestations of muscle contractions. EMG signals may be weak or at a low level when there is only a small movement in the major corresponding muscle group or when there is a strong movement in the minor corresponding muscle group. Moreover, in a single-channel EMG classification identifying the signals may be difficult. However, weak and single-channel EMG control systems offer a very convenient way of controlling human–computer interfaces (HCIs). Identifying upper-limb movements using a single-channel surface EMG also has a number of rehabilitation and HCI applications. The fractal analysis method, known as detrended fluctuation analysis (DFA), has been suggested for the identification of low-level muscle activations. This study found that DFA performs better in the classification of EMG signals from bifunctional movements of low-level and equal power as compared to other successful and commonly used features based on magnitude and other fractal techniques.

Unsupervised Bayesian Decomposition of Multiunit EMG Recordings Using Tabu Search

Intramuscular electromyography (EMG) signals are usually decomposed with semiautomatic procedures that involve the interaction with an expert operator. In this paper, a Bayesian statistical model and a maximum a posteriori (MAP) estimator are used to solve the problem of multiunit EMG decomposition in a fully automatic way. The MAP estimation exploits both the likelihood of the reconstructed EMG signal and some physiological constraints, such as the discharge pattern regularity and the refractory period of muscle fibers, as prior information integrated in a Bayesian framework. A Tabu search is proposed to efficiently tackle the nondeterministic polynomial-time-hard problem of optimization w.r.t the motor unit discharge patterns. The method is fully automatic and was tested on simulated and experimental EMG signals. Compared with the semiautomatic decomposition performed by an expert operator, the proposed method resulted in an accuracy of 90.0% +/- 3.8% when decomposing single-channel intramuscular EMG signals recorded from the abductor digiti minimi muscle at contraction forces of 5% and 10% of the maximal force. The method can also be applied to the automatic identification and classification of spikes from other neural recordings.