Multi-channel Surface Electromyography Research Articles

A randomized controlled trial investigating the impact of a single flash cupping session on patients with chronic lower back pain using multichannel surface electromyographic assessment.

Chronic lower back pain (CLBP) is one of the most common disorders worldwide. Flash cupping has the ability to relieve CLBP; nevertheless, its impact on CLBP and the likely mechanism of action have not been studied. The goal of this study was to assess the impact of a single, brief cupping session on CLBP and low back muscle activity using multichannel surface electromyography (sEMG). In this randomized controlled trial, 24patients with CLBP were enrolled and randomly assigned to the control group (treated by acupuncture) and cupping group (treated by acupuncture and flash cupping). Acupuncture was applied on the shen shu (BL23), dachang shu (BL25), and wei zhong (BL40) acupoints in both the groups. A brief cupping treatment was applied to the shen shu (BL23), qihai shu (BL24), dachang shu (BL25), guanyuan shu (BL26), and xiaochang shu (BL27) acupoints on both sides of the lower back in the cupping group. The numeric rating scale (NRS) was used to assess therapy efficacy for lower back pain (LBP) before and after treatment. Surface EMG data collected during symmetrical trunk flexion-extension movements were utilized to measure lower back muscle activity and the effectiveness of LBP therapy. There was no statistically significant difference (P= 0.63) in pain intensity between the two groups before and after treatment. There was a statistically significant difference (P= 0.04) between the control group and the cupping group in the sEMG topographic map parameter CoGx-To-Midline. This study established a connection between the action mechanism of flash cupping and enhanced horizontal synchronization of lower back muscular activity.

Loading Recognition for Lumbar Exoskeleton Based on Multi-Channel Surface Electromyography from Low Back Muscles.

Lumbar exoskeleton is an assistive robot, which can reduce the risk of injury and pain in low back muscles when lifting heavy objects. An important challenge it faces involves enhancing assistance with minimal muscle energy consumption. One of the viable solutions is to adjust the force or torque of assistance in response to changes in the load on the low back muscles. It requires accurate loading recognition, which has yet to yield satisfactory outcomes due to the limitations of available measurement tools and load classification methods. This study aimed to precisely identify muscle loading using a multi-channel surface electromyographic (sEMG) electrode array on the low back muscles, combined with a participant-specific load classification method. Ten healthy participants performed a stoop lifting task with objects of varying weights, while sEMG data was collected from the low back muscles using a 3x7 electrode array. Nineteen time segments of the lifting phase were identified, and time-domain sEMG features were extracted from each segment. Participant-specific classifiers were built using four classification algorithms to determine the object weight in each time segment, and the classification performance was evaluated using a 5-fold cross-validation method. The artificial neural network classifier achieved an impressive accuracy of up to 96%, consistently improving as the lifting phase progressed, peaking towards the end of the lifting movement. This study successfully achieves accurate recognition of load on low back muscles during the object lifting task. The obtained results hold significant potential in effectively reducing muscle energy consumption when wearing a lumbar exoskeleton.

Method for Evaluating Muscle Fatigue from Multi-channel Surface Electromyography Signals of Suprahyoid and Infrahyoid Muscles During Swallowing

Method for Evaluating Muscle Fatigue from Multi-channel Surface Electromyography Signals of Suprahyoid and Infrahyoid Muscles During Swallowing

Electrophysiology in Functional Movement Disorders: An Update.

Functional movement disorders (FMD) are a diagnostic and therapeutic challenge, both to the neurologist and psychiatrists. The phenomenology is varied and can present as tremors, dystonia, jerks/myoclonus, gait disorder, other abnormal movements or a combination. There has been an increase in the use of electrophysiological studies that are an important tool in the evaluation of FMDs. We searched the database platforms of MEDLINE, Google scholar, Web of Sciences, Scopus using the Medical Subject Heading terms (MeSH) for all the articles from 1st January 1970 till November 2022. A total of 658 articles were obtained by the search mechanism. A total of 79 relevant articles were reviewed thoroughly, of which 26 articles that had electrophysiological data were included in the present review. Variability, distractibility and entertainability can be demonstrated in functional tremors by using multichannel surface electromyography. Voluntary ballistic movements tend to decrease the tremor, while loading the tremulous limb with weight causes the tremor amplitude to increase in functional tremor. Presence of Bereitschaftspotential demonstrates the functional nature of palatal tremor and myoclonus. Co-contraction testing may be helpful in differentiating functional from organic dystonia. The R2 blink reflex recovery cycle has been found to be abnormally enhanced in organic blepharospasm, whereas it is normal in presumed functional blepharospasm. Plasticity is found to be abnormally high in organic dystonia and normal in functional dystonia, in addition to enhanced facilitation in patients with organic dystonia. Electrophysiological tests supplement clinical examination and helps in differentiating FMD from organic movement disorders.

Impact of neck percutaneous interferential current sensory stimulation on swallowing function in patients with Parkinson's disease: A single-arm, open-label study protocol

BackgroundParkinson's disease (PD) can lead to swallowing dysfunction, resulting in aspiration pneumonia. Among the types of swallowing disorders, a characteristic and serious problem associated with PD is silent aspiration due to pharyngeal and laryngeal hypoesthesia. MethodsThis single-arm, open-label study aims to evaluate the effectiveness of percutaneous neck interferential current sensory stimulation in enhancing swallowing function in patients with PD. The efficacy and safety of percutaneous neck interferential current sensory stimulation will be investigated for patients diagnosed with PD, based on the Movement Disorder Society criteria, of Hoehn–Yahr stages 2–4. The patients will receive neck percutaneous interferential current sensory stimulation for 20 min twice a week for 8 weeks using a Gentle Stim® (FoodCare Co., Ltd., Kanagawa, Japan) device. Once the intervention is initiated, evaluations will be performed every 4 weeks for a 16-week period. The primary endpoint to be assessed is the proportion of patients with normal cough with 1% citric acid at the end of the intervention (8 weeks after intervention initiation) compared with that at the beginning. This clinical trial will examine the usefulness of percutaneous neck interferential current sensory stimulation in patients with PD. In addition, this study will use novel instruments, such as multichannel surface electromyography and electronic stethoscope, to evaluate swallowing function. DiscussionThis novel evaluation can provide insights into dysphagia in patients with PD and the usefulness of percutaneous neck interferential current stimulation. This exploratory study is limited by its single-arm, open-label design and small size. Trial registration numberjRCTs062220013; pre-results.

Study of the relationship between muscle thickness and the conducting waves of multichannel surface electromyography

The contraction of skeletal muscle is regulated by the number of motor units (MUs) and muscle fibre types. The analysis of MU recruitment is feasible for elucidating the mechanisms of muscular contraction and diagnosing muscular diseases. Therefore, various studies using multichannel surface electromyography (EMG) have been conducted. In our previous study, we proposed a method to search for all conducting waves appearing in multichannel surface EMG quantitatively by calculating the similarity of waveforms. By examining conducting waves, it may be possible to estimate MU recruitment. However, the certainty of such results depends solely on considerations of anatomical and physiological knowledge. One quantitative evaluation of muscle thickness is its measurement using ultrasound imaging. In this study, we investigated the feasibility of conducting wave analysis by examining the relationship between conducting waves and muscle thickness which can be an indicator of muscle hypertrophy or wasting. Multichannel surface EMG and muscle thickness measurements were conducted and on the biceps brachii muscles of 20 subjects and the results were analysed. We found that the mean amplitude and velocity of the conducting waves were proportional to bicep thickness, indicating that conducting wave analysis corresponds to muscle thickness as a quantitative index.

Activity Distribution Among the Hamstring Muscles During the Nordic Hamstring Exercise: A Multichannel Surface Electromyography Study.

This study assessed activity distribution among the hamstring muscles during the Nordic hamstring exercise (NHE). The objective was to compare muscle activity between and within muscles during the NHE to add insights in its underlying protective mechanism. Through multichannel electromyography, we measured muscle activity in male basketball players during the NHE. Electromyography was assessed at 15 locations: 5 for biceps femoris long head, 4 for semitendinosus, and 6 for semimembranosus. For each percent of the eccentric phase of the NHE, muscle activity was calculated for each electrode location within each hamstring muscle individually. To quantify whole muscle head activity, means and variances across electrodes within each muscle were calculated. Thirty-five noninjured participants were included (mean age, 18 [2]y; mass, 87 [12]kg; height, 192 [9]cm). Heterogeneous muscle activity was found between 38% and 62% and over the whole eccentric contraction phase within the semitendinosus and the semimembranosus, respectively. Muscle activity of the semitendinosus was significantly higher than that of the biceps femoris long head. During the NHE, the relative contribution of the semitendinosus is the highest among hamstring muscles. Its strong contribution may compensate for the biceps femoris long head, the most commonly injured hamstring muscle head.

Multifunctional Evaluation Technology for Diagnosing Malfunctions of Regional Pelvic Floor Muscles Based on Stretchable Electrode Array Probe.

The pelvic floor dysfunction (PFD) has become a serious public health problem. Accurate diagnosis of regional pelvic floor muscle (PFM) malfunctions is vitally important for the prevention and treatment of PFD. However, there is a lack of reliable diagnostic devices to evaluate and diagnose regional PFM abnormality. In this work, we developed a multifunctional evaluation technology (MET) based on a novel airbag-type stretchable electrode array probe (ASEA) for the diagnosis of malfunctions of regional PFM. The inflatable ASEA has specifically distributed 32 electrodes along the muscles, and is able to adapt to different human bodies for tight contact with the muscles. These allow synchronous collection of high-quality multi-channel surface electromyography (MC-sEMG) signals, and then are used to diagnose regional PFM malfunctions and evaluate inter-regional correlation. Clinical trial was conducted on 15 postpartum stress urinary incontinence (PSUI) patients and 15 matched asymptomatic women. Results showed that SUI patients responded slowly to the command and have symptoms of muscle strength degeneration. The results were consistent with the relevant clinical manifestations, and proved the reliability of MET for multifunctional PFM evaluation. Furthermore, the MET can diagnose malfunctions of regional PFM, which is inaccessible with existing technology. The results also showed that the dysfunction of PSUI patients is mainly located in iliococcygeus, pubococcygeus, and urethral sphincter regions, and there is a weak correlation between these specific regions and nearby regions. In conclusion, MET provides a point-of-care diagnostic method for abnormal function of regional PFM, which has a potential for the targeted point-to-point electrical stimulation treatment and PFD pathology research.

SEMG Wireless Acquisition System Based on CC3200 and ADS1299

A multi-channel surface electromyography wireless acquisition system is designed, which is mainly composed of ADS1299 integrated analog front-end chip and CC3200 wireless MCU of TI company. The key indicators of hardware are measured according to the industry standard, and the results are better than the industry standard, which can meet the continuous use of multi-scene tasks. This system has the advantages of high performance, low power consumption and small size. It has been applied to the detection of surface EMG signal in motion gesture recognition and has a good application value.

EMAHA-DB1: A New Upper Limb sEMG Dataset for Classification of Activities of Daily Living

In this paper, we present electromyography analysis of human activity - database 1 (EMAHA-DB1), a novel dataset of multi-channel surface electromyography (sEMG) signals to evaluate the activities of daily living (ADL). The dataset is acquired from 25 able-bodied subjects while performing 22 activities categorised according to functional arm activity behavioral system (FAABOS) (3 - full hand gestures, 6 - open/close office draw, 8 - grasping and holding of small office objects, 2 - flexion and extension of finger movements, 2 - writing and 1 - rest). The sEMG data is measured by a set of five Noraxon Ultium wireless sEMG sensors with Ag/Agcl electrodes placed on a human hand. The dataset is analyzed for hand activity recognition classification performance. The classification is performed using six state-of-the-art machine learning classifiers, including Random Forest (RF), Fine K-Nearest Neighbour (KNN), Ensemble KNN (sKNN), Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), and a hybrid deep learning architecture of Convolutional Neural Network (CNN) and Bidirectional- Long Short Term Memory (Bi-LSTM) layers. In the classical methods, ten combinations of time domain and frequency domain feature sets are analyzed. The state-of-the-art classification accuracy on five FAABOS categories is 83.21% by using the SVM classifier with the third order polynomial kernel using energy feature and auto regressive feature set ensemble. The classification accuracy on 22 class hand activities is 75.39% by the same SVM classifier with the log moments in frequency domain (LMF) feature, modified LMF, time domain statistical (TDS) feature, spectral band powers (SBP), channel cross correlation and local binary patterns (LBP) set ensemble. The analysis depicts the technical challenges addressed by the dataset. The developed dataset can be used as a benchmark for various classification methods as well as for sEMG signal analysis corresponding to ADL and for the development of prosthetics and other wearable robotics.

SEMG-Based Gesture Recognition Using Deep Learning From Noisy Labels.

Gesture recognition for myoelectric prosthesis control utilizing sparse multichannel surface Electromyography (sEMG) is a challenging task, and from a Muscle-Computer Interface (MCI) standpoint, the performance is still far from optimal. However, the design of a well-performed sEMG recognition system depends on the flexibility of the input-output function and the dataset's quality. To improve the performance of MCI, we proposed a novel gesture recognition framework that (i) Enrich the spectral information of the sparse sEMG signals by constructing a fused map image (denoted as sEMG-Map) that integrates a multiresolution decomposition (by means of orthogonal wavelets) through the raw signals then rely upon the Convolutional Neural Network (CNN) capacity to exploit the composite hierarchies in the constructed sEMG-Map input. (ii) Deals with the label noise by proposing a data-centric method (denoted as ALR-CNN) that synchronously refines the falsely labeled samples and optimizes the CNN model based on two basic assumptions. First, the deep model accuracy improves as the training progress. Second, a set of successive learnable max-activated outputs of a well-performed deep model is a reliable estimator for motion detection in the muscle activation pattern. Our proposed framework is evaluated on three large-scale public databases. The average classification accuracy is 95.50%, 95.85%, and 85.58% for NinaPro DB2, NinaPro DB7, and NinaPro DB3, respectively. The experimental results verify the effectuality of the proposed method and show high accuracy.

Evaluation method of muscle activity distribution by multi-channel surface electromyography using bullseye electrodes

Evaluation method of muscle activity distribution by multi-channel surface electromyography using bullseye electrodes

Myoclonus and tremor in chronic inflammatory demyelinating polyneuropathy: a multichannel electromyography analysis.

Information regarding involuntary movements in chronic inflammatory polyneuropathy (CIDP) is gradually increasing. Our goal is to identify the types of involuntary movements in CIDP. All patients who were followed with the diagnosis of CIDP were invited for clinical and electrophysiological evaluations. Demographic and clinical findings (age, gender, duration of illness, diagnosis, treatments) were noted. Clinical examination and multichannel surface electromyography were done. We also performed routine upper and lower extremity peripheral nerve conduction studies, F-waves, long latency reflexes, blink reflex, mixed nerve silent period and cutaneous silent period in all patients. Twenty-two patients accepted the invitation. Eleven patients with CIDP had involuntary movements. Ten (45.5%) patients with CIDP had tremor and seven (31.8%) had short-duration and high-amplitude myoclonus. Regarding demographic, clinical and electrophysiological features, there was no significant difference between patients with and without tremor. The latencies of R1, R2 and R2c components of BR were longer among CIDP patients without tremor compared to CIDP patients with tremor. Presence of myoclonus (p = 0.007) and delayed F-waves (p = 0.008) were associated with the presence of tremor. Tremor and myoclonus were frequent in CIDP. The fact that myoclonus was detected in the majority of patients only by multichannel surface EMG who were clinically evaluated as pure tremor suggests that a more detailed electrophysiological evaluation is required. There was no difference in the medications used or other clinical features between patients with and without tremor.

Neuromuscular Adjustments of the Quadriceps Muscle After Eccentric Resistance and Concentric Resistance Training

Introduction: Deficiency in the neural control of movement is associated with poor posture and skeletal muscle injuries. Exercise training is commonly reported as an intervention to improve neuromuscular activity. However, maximizing the effectiveness of exercise interventions for improving neural control of movement has been less investigated. The purpose of the current study was to examine improvement in neuromuscular activity (e.g., muscle fiber conduction velocity) and quadriceps function after eccentric resistance training versus concentric training.
 Materials and Methods: A total of 24 men participated in this study and were randomly divided into eccentric training (n=12) and concentric training groups (n=12). Maximal voluntary isometric contraction (MIVC) of quadriceps, vertical jumping, and multichannel surface electromyography (EMG) signals were recorded before and 12 weeks after resistance eccentric and concentric training. Muscle fiber conduction velocity (MFCV) and root mean square (RMS) were computed using raw EMG signals.
 Results: The percentage increases in MVIC and vertical jumping after eccentric resistance training were significantly higher than those after concentric training (P<0.05). Likewise, eccentric exercise resulted in a higher increase in MFCV and RMS of EMG than concentric exercise (P<0.05).
 Conclusion: A higher increase in neuromuscular activity and quadriceps performance observed after eccentric exercise may indicate that eccentric resistance training is more effective in improving neuromuscular activity and muscle function.

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.

Tremor and Myoclonus is Common in Immune-Mediated and Hereditary Polyneuropathies

Objective: Our goal was to identify the frequency and types of involuntary movements in immune mediated and hereditary polyneuropathies. Methods: In this prospective study, we included all consecutive patients with immune mediated or hereditary polyneuropathy between January 2017 and November 2019. The presence and type of the involuntary movements were determined by the clinical examination and multichannel surface electromyography. Results: We identified 23 (48.9%) patients with involuntary movements among 47 patients with immune mediated or hereditary polyneuropathy in the study period. All patients with an involuntary movement had postural tremor with accompanying action and/or rest tremor. Short duration and high-amplitude myoclonus was accompanying in 18 (38.3%) patients. The demographic and clinical characteristics and features of nerve conduction studies were similar between patients with and without involuntary movements. Discussion: Tremor and myoclonus were frequent in our cohort. Postural tremor was the most frequent subtype. There was no significant relationship between myoclonus or tremor and clinical/electrophysiological features. Conclusion: For assessing movement disorders in polyneuropathies not only clinical examination, but also electrophysiological studies such as multichannel surface electromyography should be used.

Research on sEMG-Based Gesture Recognition by Dual-View Deep Learning

In the field of human-machine interaction, gesture recognition using sparse multichannel surface electromyography (sEMG) remains a challenge. Based on the Hilbert filling curve, a dual-view multi-scale convolutional neural network (DVMSCNN) is designed to enhance gesture recognition performance in this paper. The network consists of two parts. In the first part, sEMG is filled using Hilbert filling curve, and the obtained images in the time and electrode domain are used as inputs to the block. In the second part, the depth features learned by block are fused and classified by a “layer fusion” based view aggregation network. The evaluation of the architecture in the four databases of Ninapro-DB1, DB2, DB3 and DB4 shows that DVMSCNN is more than 7% more accurate than other state-of-the-art methods. When validated using a home-grown dataset, DVMSCNN was able to achieve a recognition rate of 0.8848.

Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors.

Finger gesture recognition (FGR) plays a crucial role in achieving, for example, artificial limb control and human-computer interaction. Currently, the most common methods of FGR are visual-based, voice-based, and surface electromyography (EMG)-based ones. Among them, surface EMG-based FGR is very popular and successful because surface EMG is a cumulative bioelectric signal from the surface of the skin that can accurately and intuitively represent the force of the fingers. However, existing surface EMG-based methods still cannot fully satisfy the required recognition accuracy for artificial limb control as the lack of high-precision sensor and high-accurate recognition model. To address this issue, this study proposes a novel FGR model that consists of sensing and classification of surface EMG signals (SC-FGR). In the proposed SC-FGR model, wireless sensors with high-precision surface EMG are first developed for acquiring multichannel surface EMG signals from the forearm. Its resolution is 16 Bits, the sampling rate is 2 kHz, the common-mode rejection ratio (CMRR) is less than 70 dB, and the short-circuit noise (SCN) is less than 1.5 μV. In addition, a convolution neural network (CNN)-based classification algorithm is proposed to achieve FGR based on acquired surface EMG signals. The CNN is trained on a spectrum map transformed from the time-domain surface EMG by continuous wavelet transform (CWT). To evaluate the proposed SC-FGR model, we compared it with seven state-of-the-art models. The experimental results demonstrate that SC-FGR achieves 97.5% recognition accuracy on eight kinds of finger gestures with five subjects, which is much higher than that of comparable models.

Effect of arm position on spatial distribution of upper trapezius muscle activity during simulated car driving

The present study aimed to investigate the upper trapezius muscle activity during simulated car driving while adopting three different arm positions. Ten participants were instructed to maintain the following positions: hands on the steering wheel (Hands-On), hands not on the steering wheel (Hands-Off) and hands not on the steering wheel but arms on armrests (Armrests). During the tasks, multi-channel surface electromyography (EMG) was recorded from the upper trapezius muscle with 64 two-dimensionally distributed electrodes. Amplitudes of surface EMG in Armrests were lower than in Hands-On (p = 0.004). The spatial distribution of surface EMG changed with time in Hands-Off and Armrests (p < 0.05), but not in Hands-On (p > 0.05). These findings suggest that being freed from steering leads to the recruitment of various muscle fibers/motor units within the upper trapezius muscle and the use of armrests may help reduce the physiological burden loaded on the muscle of drivers.

Influence of hybrid assistive limb gait training on spatial muscle activation patterns in spinal muscular atrophy type III

Background: Despite the potential benefits, the effects of Hybrid Assistive Limb (HAL) gait training on changes in neuromuscular activation that accompany functional gains in individuals with spinal muscular atrophy (SMA) type III is not well known. In this article, we quantify the effects of HAL gait training on spatial muscle activity patterns in a patient with SMA type III using multi-channel surface electromyography (SEMG). Methods: A 21-years old male (168 cm, 47.8 kg) with spinal muscular atrophy type III, when diagnosed at 18-years old by genetic screening, participated in this case study. Although he presented with forearm distal muscle weakness, atrophy of the intrinsic muscles of the hand, and neuromuscular fatigue, his activities of daily living is independent. The patient underwent a separate, single 33-minute session of both HAL and treadmill gait training. To evaluate the coefficient of variation (CoV) of force and alterations in the SEMG spatial distribution patterns, modified entropy and CoV of root mean square (RMS) were calculated from the vastus lateralis (VL) muscle before and after the intervention of HAL and treadmill gait training. Each training session was separated by a period of one month to avoid cross-over effects. Results: There was a greater decrease in the ΔCoV of force and an increase in the magnitude of whole VL muscle activation from pre-intervention to post-intervention with the HAL gait training as compared to the treadmill gait training. In response to only HAL gait training, the CoV of RMS was higher, and the modified entropy was lower post-intervention than pre-intervention. Conclusions: Our results support the notion that HAL gait training has a positive benefit on motor output not only in the magnitude of SEMG generated but also the patterns of neural activation.