Single-channel Surface Electromyography Research Articles

Relative contribution of neuromuscular activation, muscle size, and muscle quality to maximum strength output of the thigh muscles in young individuals.

This study aimed to investigate the relationship between maximal muscle strength and neuromuscular activation, muscle size, and quality of quadriceps (QF) and hamstring muscles (HM). The study included 24 young men and women. The neuromuscular activation parameter was recorded using a single-channel surface electromyography (EMG) with the root mean square (RMS) during maximal isometric knee extension and flexion from four muscles: rectus femoris and vastus lateralis for QF; biceps femoris and semitendinosus for HM. In addition, the peak torque was measured during the same session. B-mode ultrasonographic transverse images were obtained from the anterior, lateral, and posterior thighs. Furthermore, we calculated the muscle thickness (MT) and echo intensity (EI) of the four muscles as indicators of muscle size and quality. The averaged MT, EI, and absolute RMS of QF were calculated by averaging the values of the rectus femoris and vastus lateralis, and that of HM was calculated by averaging the values of the biceps femoris and semitendinosus. The knee extension peak torque was correlated with EI (r=-0.61, P < 0.01) and RMS (r=0.53, P < 0.01) in the QF. In contrast, the knee flexion peak torque was correlated with RMS (r=0.53, P < 0.05) but not with MT and EI in HM. In addition, EI and RMS in QF, and RMS in HM were selected as the major determinants of muscle strength in the stepwise regression analysis. These results suggest that muscle strength is moderately associated with different factors related to the thigh muscles in young individuals.

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.

Single-channel surface electromyography signal classification with variational mode decomposition and entropy feature for lower limb movements recognition

Currently, many researchers tend to use multi-channel surface electromyography (sEMG) signals to improve the accuracy of lower limb movement recognition. However, the collection of multi-channel sEMG signals will reduce the usability of wearable devices for lower limbs based on sEMG signals in amputees, patients with impaired muscle function, and the disabled. How to effectively use single-channel sEMG signals to achieve better recognition performance is a difficult problem to improve the usability of wearable devices based on sEMG signals. In this research, we proposed a precise feature extraction method for single-channel sEMG signals to achieve accurate recognition of lower limb movements. The single-channel sEMG signal was decomposed into multiple variational modal functions (VMF) through variational mode decomposition (VMD), and entropy features were extracted from VMFs to highlight the prominent information of the sEMG signal. Entropy features with statistical differences were selected by the Kruskal-Wallis test. Four lower limb movements were recognized through machine learning. Moreover, the recognition performance exhibited by the proposed method on the sEMG signal of two different muscles was evaluated. The sEMG signals of four lower limb movements from twenty subjects recorded by the wearable sEMG signal sensor were employed to test the proposed method. The experimental results showed that the accuracy of the proposed method for the sEMG signals of two different muscles reached 95.82% and 97.44%. This research concluded that the proposed method is promising to improve the usability of wearable devices based on sEMG signals in amputees, patients with impaired muscle function, and the disabled.

Single-channel surface electromyography (sEMG) based control of a multi-functional prosthetic hand

Patients suffering from upper limb amputation experience a significant reduction in their ability to perform various tasks. The currently available myoelectric prostheses seek to reinstate the lost abilities of the amputees. However, most users abandon their devices due to high cost, large size, heavy weight, complexity, and limited functional control. This paper presents a multifunctional prosthetic hand that can perform six hand activities deploying a single-channel surface electromyography (sEMG) sensor. EMG signals for fifteen subjects (five amputees and ten intact) were acquired for six contraction levels of forearm muscles using the designed sensor. These levels were further classified to recognize six predefined hand gestures by a fuzzy logic classifier. The proposed system showed an excellent success (> 95%) and other performance parameters above 96%. The intended classification-based control scheme was further realized in real-time to achieve six grip patterns for the developed prosthetic prototype. The hand was tested on five subjects (two amputees and three intact) showed a percentage of success above 91% for accomplishing the dexterous grasping operations. The proposed approach is modest, efficient, and provides a low-cost solution to amputees with multiple degrees of freedom with a single channel EMG system.

A Minimal Setup for Spontaneous Smile Quantification Applicable for Valence Detection.

Tracking emotional responses as they unfold has been one of the hallmarks of applied neuroscience and related disciplines, but recent studies suggest that automatic tracking of facial expressions have low validation. In this study, we focused on the direct measurement of facial muscles involved in expressions such as smiling. We used single-channel surface electromyography (sEMG) to evaluate the muscular activity from the Zygomaticus Major face muscle while participants watched music videos. Participants were then tasked with rating each video with regard to their thoughts and responses to each of them, including their judgment of emotional tone (“Valence”), personal preference (“Liking”) and rating of whether the video displayed strength and impression (“Dominance”). Using a minimal recording setup, we employed three ways to characterize muscular activity associated with spontaneous smiles. The total time spent smiling (ZygoNum), the average duration of smiles (ZygoLen), and instances of high valence (ZygoTrace). Our results demonstrate that Valence was the emotional dimension that was most related to the Zygomaticus activity. Here, the ZygoNum had higher discriminatory power than ZygoLen for Valence quantification. An additional investigation using fractal properties of sEMG time series confirmed previous studies of the Facial Action Coding System (FACS) documenting a smoother contraction of facial muscles for enjoyment smiles. Further analysis using ZygoTrace responses over time to the video events discerned “high valence” stimuli with a 76% accuracy. Additional validation of this approach came against previous findings on valence detection using features derived from a single channel EEG setup. We discuss these results in light of both the recent replication problems of facial expression measures, and in relation to the need for methods to reliably assess emotional responses in more challenging conditions, such as Virtual Reality, in which facial expressions are often covered by the equipment used.

Appropriate window width for the “clustering index method” in the tibialis anterior muscle

We previously reported a new quantitative analysis of single-channel surface electromyography (EMG), the "clustering index method" (CI method), in the tibialis anterior muscle, which achieved sufficiently good sensitivity to detect neurogenic or myogenic abnormalities. The window width is a fundamental parameter of the CI method, and was arbitrarily set at 15 ms in that study. In this study, we searched for the most appropriate window width using expanded patient data. The data from our previous study were reanalyzed, and new patients were enrolled. Window width in the CI method was changed from 5 to 27.5 ms with a step of 2.5 ms. For each window width, Z-score values of individual subjects were calculated and the diagnostic yield was investigated. We enrolled 67 controls, 29 subjects with neurogenic disorders, and 39 with myogenic disorders. When the window width was set at 22.5 ms, the highest sensitivity was achieved both for neurogenic (97%) and myogenic (72%) disorders, with a specificity of 97%. Seven of 10 patients with inclusion body myositis were also abnormal. Reliable results were obtained by collecting 15 epochs per subject. There are two conflicting effects that appear to be best balanced at a window width of 22.5 ms: a wider width decreases the chance that a motor unit potential (MUP) is divided into two adjacent windows, and a narrower width reduces the possibility that an MUP firing at a low-frequency is counted twice by the differential sequences. CI is promising as a non-invasive method to diagnose neuromuscular disorders.

Differentiation of Epileptic and Psychogenic Nonepileptic Seizures Using Single-Channel Surface Electromyography.

Epileptic seizures (ES) and psychogenic nonepileptic seizures (PNES) are difficult to differentiate when based on a patient's self-reported symptoms. This study proposes review of objective data captured by a surface electromyography (sEMG) wearable device for classification of events as ES or PNES. This may help clinicians accurately identify ES and PNES. Seventy-one subjects were prospectively enrolled across epilepsy monitoring units at VA Epilepsy Centers of Excellence. Subjects were concomitantly monitored using video EEG and a wearable sEMG epilepsy monitor, the Sensing Portable sEmg Analysis Characterization (SPEAC) System. Three epileptologists independently classified ES and PNES that contained upper extremity motor activity based on video EEG. The sEMG data from those events were automatically processed to provide a seizure score for event classification. After brief training (60 minutes), the sEMG data were reviewed by a separate group of four epileptologists to independently classify events as ES or PNES. According to video EEG review, 17 subjects experienced 34 events (15 ES and 19 PNES with upper extremity motor activity). The automated process correctly classified 87% of ES (positive predictive value = 88%, negative predictive value = 76%) and 79% of PNES, and the expert reviewers correctly classified 77% of ES (positive predictive value = 94%, negative predictive value = 84%) and 96% of PNES. The automated process and the expert reviewers correctly classified 100% of tonic-clonic seizures as ES, and 71 and 50%, respectively, of non-tonic-clonic ES. Automated and expert review, particularly in combination, of sEMG captured by a wearable seizure monitor (SPEAC System) may be able to differentiate ES (especially tonic-clonic) and PNES with upper extremity motor activity.

Single-channel SEMG using wavelet deep belief networks for upper limb motion recognition

Surface electromyography (SEMG) has been widely used in different fields such as human machine interaction and motion recognition. A hybrid classification model based on singular value decomposition (SVD) and wavelet deep belief networks (WDBN) is firstly proposed in this paper, which allows the machine to recognize the single-joint motions of upper limb by using one channel. In this experiment, the three-joint SEMG signals of upper limb are respectively recorded through different two channels, which are employed for subsequent comparison to obtain the best single-channel of each joint. Afterwards, the collected raw signals are enhanced by SVD processing. Wavelet function is applied to replace sigmoid function as activation function for feature learning, and the spectrum signals processed by fast Fourier transform (FFT) are input to WDBN model. The results demonstrate that the recognition rates of three joint movements can be up to 100% by SVD-WDBN method, which is much better than support vector machine (SVM), back propagation (BP) neural network and extreme learning machine (ELM) model. The proposed method makes it more possible to control wearable devices with different single-channel SEMG signals, thereby the work efficiency of smart wearable devices can be improved, as well as the complexity of operations between human and machine can be reduced.

Locomo-Net: A Low -Complex Deep Learning Framework for sEMG-Based Hand Movement Recognition for Prosthetic Control.

Background: The enhancement in the performance of the myoelectric pattern recognition techniques based on deep learning algorithm possess computationally expensive and exhibit extensive memory behavior. Therefore, in this paper we report a deep learning framework named ‘Low-Complex Movement recognition-Net’ (LoCoMo-Net) built with convolution neural network (CNN) for recognition of wrist and finger flexion movements; grasping and functional movements; and force pattern from single channel surface electromyography (sEMG) recording. The network consists of a two-stage pipeline: 1) input data compression; 2) data-driven weight sharing. Methods: The proposed framework was validated on two different datasets- our own dataset (DS1) and publicly available NinaPro dataset (DS2) for 16 movements and 50 movements respectively. Further, we have prototyped the proposed LoCoMo-Net on Virtex-7 Xilinx field-programmable gate array (FPGA) platform and validated for 15 movements from DS1 to demonstrate its feasibility for real-time execution. Results: The effectiveness of the proposed LoCoMo-Net was verified by a comparative analysis against the benchmarked models using the same datasets wherein our proposed model outperformed Twin- Support Vector Machine (SVM) and existing CNN based model by an average classification accuracy of 8.5 % and 16.0 % respectively. In addition, hardware complexity analysis is done to reveal the advantages of the two-stage pipeline where approximately 27 %, 49 %, 50 %, 23 %, and 43 % savings achieved in lookup tables (LUT’s), registers, memory, power consumption and computational time respectively. Conclusion: The clinical significance of such sEMG based accurate and low-complex movement recognition system can be favorable for the potential improvement in quality of life of an amputated persons.

Automated Processing of Single-Channel Surface Electromyography From Generalized Tonic-Clonic Seizures to Inform Semiology.

Advances in surface electromyography (sEMG) monitoring allow for long-term data collection in a natural environment, giving objective information that may identify risk of sudden unexpected death in epilepsy and guide clinical decision-making. Generalized tonic-clonic seizure semiology, namely motor tonic and clonic phase duration, may be an important factor in determining the level of seizure control and risk of sudden unexpected death in epilepsy. This study demonstrates a quantitative analysis of sEMG collected with a dedicated wearable device. During routine monitoring, 23 generalized tonic-clonic seizures from 19 subjects were simultaneously recorded with video-EEG and sEMG. A continuous wavelet-transform was used to determine the frequency components of sEMG recorded during generalized tonic-clonic seizures. An automated process, incorporating a variant of cross-validation, was created to identify ideal frequencies and magnitude ranges for tonic and clonic phases and determine phase durations. Phase durations determined using sEMG analysis were compared with phase durations determined by independent epileptologists' review of video-EEG. Cross-validation revealed that the optimal frequency bands for tonic and clonic phases are 150 to 270 Hz and 12 to 70 Hz, respectively. The average difference in phase duration calculated using the two methods for tonic and clonic phases and total seizure duration were -0.42 ± 4.94, -5.12 ± 9.68, and -5.11 ± 11.33 seconds, respectively (results presented are TsEMG - TvEEG, μ ± σ). The automated processing of sEMG presented here accurately identified durations of tonic, clonic, and total motor durations of generalized tonic-clonic seizures similar to durations identified by epileptologists' review of video-EEG.

A novel fuzzy approach for automatic Brunnstrom stage classification using surface electromyography.

Clinical assessment plays a major role in post-stroke rehabilitation programs for evaluating impairment level and tracking recovery progress. Conventionally, this process is manually performed by clinicians using chart-based ordinal scales which can be both subjective and inefficient. In this paper, a novel approach based on fuzzy logic is proposed which automatically evaluates stroke patients' impairment level using single-channel surface electromyography (sEMG) signals and generates objective classification results based on the widely used Brunnstrom stages of recovery. The correlation between stroke-induced motor impairment and sEMG features on both time and frequency domain is investigated, and a specifically designed fuzzy kernel classifier based on geometrically unconstrained membership function is introduced in the study to tackle the challenges in discriminating data classes with complex separating surfaces. Experiments using sEMG data collected from stroke patients have been carried out to examine the validity and feasibility of the proposed method. In order to ensure the generalization capability of the classifier, a cross-validation test has been performed. The results, verified using the evaluation decisions provided by an expert panel, have reached a rate of success of the 92.47%. The proposed fuzzy classifier is also compared with other pattern recognition techniques to demonstrate its superior performance in this application.

Towards identification of finger flexions using single channel surface electromyography – able bodied and amputee subjects

BackgroundThis research has established a method for using single channel surface electromyogram (sEMG) recorded from the forearm to identify individual finger flexion. The technique uses the volume conduction properties of the tissues and uses the magnitude and density of the singularities in the signal as a measure of strength of the muscle activity.MethodsSEMG was recorded from the flexor digitorum superficialis muscle during four different finger flexions. Based on the volume conduction properties of the tissues, sEMG was decomposed into wavelet maxima and grouped into four groups based on their magnitude. The mean magnitude and the density of each group were the inputs to the twin support vector machines (TSVM). The algorithm was tested on 11 able-bodied and one trans-radial amputated volunteer to determine the accuracy, sensitivity and specificity. The system was also tested to determine inter-experimental variations and variations due to difference in the electrode location.ResultsAccuracy and sensitivity of identification of finger actions from single channel sEMG signal was 93% and 94% for able-bodied and 81% and 84% for trans-radial amputated respectively, and there was only a small inter-experimental variation.ConclusionsVolume conduction properties based sEMG analysis provides a suitable basis for identifying finger flexions from single channel sEMG. The reported system requires supervised training and automatic classification.

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.

Single channel surface electromyography blind recognition model based on watermarking

A single channel electromyography blind recognition model based on watermarking is proposed in this paper. Single Channel Independent Component Analysis is adopted to avoid complicated circuit connection and the unreliability of hardware and reduce the noise which accompanied with surface Electromyography (sEMG) signals. Embedded watermarking is applied to solve the problem of blind source separation disorder. A self adaptive neural network and some eigenvectors are applied in sEMG features classification. From the classification results, hand gestures can be recognized. In consideration of time-scale synchronization attack, the host sEMG signals are transformed into wavelet domain and the synchronization codes are embedded. The experiment results show that the model proposed in this paper is penetrable against most common signal processing, and can recognize the hand gesture accurately.

Motor unit action potential topography and its use in motor unit number estimation

High-density multichannel electromyography (EMG) recordings add spatial information to the temporal information content of the surface EMG (sEMG) signal. This study explores the potential value of such multichannel information at a single motor unit level, in particular for the improvement of motor unit number estimation (MUNE) techniques. It is shown that multichannel recordings make it easier to distinguish motor unit action potentials (MUAPs) and that MUAP combinations can be better resolved. Furthermore, the spatiotemporal information allows a quantitative assessment of the representativity of the mean of the recorded MUAPs in relation to the maximum CMAP, i.e., for the muscle as a whole. In general, this is practically impossible on the basis of temporal information alone. For these reasons, we expect that high-density sEMG has the potential to address several methodological limitations of single channel surface EMG recordings. This is specifically illustrated in this study for one of the MUNE techniques in use, the F-response method.