Multichannel Surface Electromyogram Research Articles

Impact of neck percutaneous interferential current sensory stimulation on swallowing function in patients with Parkinson's disease

Dysphagia is the clinical name for difficulty with swallowing. It can occur with some neurological diseases such as Parkinsonâ–™s disease and, given the rapidly ageing population, the numbers of patients with Parkinsonâ–™s disease and dysphagia is on the rise. Despite being quite commonplace, the complex mechanisms of dysphagia mean that the condition is not well understood. Assistant Professor Masahiro Nakamori is a clinical neurologist investigating the mechanisms of neurological disease, with a focus on neurodegenerative diseases and dysphagia. A current focus is on dysphagia and silent aspiration in people with Parkinsonâ–™s disease. He is based in the Department of Clinical Neuroscience and Therapeutics, Graduate School of Biomedical and Health Sciences, Hiroshima University, Japan. So far, Nakamori has explored several different techniques, including tongue pressure measurement and tongue ultrasonography, which are both used in swallowing evaluation. He is interested in non-invasive techniques that are simple to implement and wants to develop new creative instruments and techniques. In his current research, Nakamori and his collaborators are experimenting with multichannel surface electromyogram to gain a comprehensive understanding of neuromuscular activity. The goal is to develop new techniques and instruments that will help advance clinical neurology and dysphagia research and, of course, benefit patients; improving outcomes and quality of life.

Estimation of Muscle Fiber Direction by Multi-channel Surface EMG Conductiong Wave Analysis Using Grid Surface Electrodes

Skeletal muscle is a set of motor units (MU). Muscle contractile activity is carried out by regulating the firing frequency of MU and the types of muscle fibers to be mobilized. Multi-channel surface electromyogram (sEMG) contains many conducting waves that represent a single motor unit action potentials (MUAP). In previous study, we proposed a method to extract conducting waves quantitatively and automatically. This method is useful for elucidating mobilized MUs and can be applied to kinematic analysis and diagnosis of muscle diseases. In multi-channel sEMG measurement, rows of electrodes need to be applied along the direction of the muscle fibers. However, the electrodes are difficult to set because the direction of the muscle fibers cannot be visually confirmed on the skin. This study investigated a method for estimating muscle fiber direction by analyzing conducting waves from two-dimensional multichannel surface electromyograms using grid-shaped surface electrodes. By examining the number of conducting waves acquired in each direction of the electrode row, it was suggested that the direction of muscle fiber may be estimated from the direction with the largest number of propagating wave acquisitions. It was also found that the method of acquiring differential potential signals has a significant impact on the analysis of propagation direction.

Estimation of Time-Frequency Muscle Synergy in Wrist Movements.

Muscle synergy analysis is a kind of modularized decomposition of muscles during exercise controlled by the central nervous system (CNS). It can not only extract the synergistic muscles in exercise, but also obtain the activation states of muscles to reflect the coordination and control relationship between muscles. However, previous studies have mainly focused on the time-domain synergy without considering the frequency-specific characteristics within synergy structures. Therefore, this study proposes a novel method, named time-frequency non-negative matrix factorization (TF-NMF), to explore the time-varying regularity of muscle synergy characteristics of multi-channel surface electromyogram (sEMG) signals at different frequency bands. In this method, the wavelet packet transform (WPT) is used to transform the time-scale signals into time-frequency dimension. Then, the NMF method is calculated in each time-frequency window to extract the synergy modules. Finally, this method is used to analyze the sEMG signals recorded from 8 muscles during the conversion between wrist flexion (WF stage) and wrist extension (WE stage) movements in 12 healthy people. The experimental results show that the number of synergy modules in wrist flexion transmission to wrist extension (Motion Conversion, MC stage) is more than that in the WF stage and WE stage. Furthermore, the number of flexor and extensor muscle synergies in the frequency band of 0–125 Hz during the MC stage is more than that in the frequency band of 125–250 Hz. Further analysis shows that the flexion muscle synergies mostly exist in the frequency band of 140.625–156.25 Hz during the WF stage, and the extension muscle synergies appear in the frequency band of 125–156.25 Hz during the WE stage. These results can help to better understand the time-frequency features of muscle synergy, and expand study perspective related to motor control in nervous system.

The Evaluation of Classifier Performance during Fitting Wrist and Finger Movement Task Based on Forearm HD-sEMG

The transmission of human body movement signals to other devices through wearable smart bracelets has attracted increasing attention in the field of human-machine interfaces. However, owing to the limited data collection range of wearable bracelets, it is necessary to study the relationship between the superposition of the wrist and fingers and their cooperative motions to simplify the data collection system of such devices. Multichannel high-density surface electromyogram (HD-sEMG) signals exhibit high spatial resolutions, and they can help improve the accuracy of the multichannel fitting. In this study, we quantified the HD-sEMG forearm spatial activation features of 256 channels of hand movement and performed a linear fitting of the data obtained for finger and wrist movements in order to verify the linear superposition relationship between the cooperative and independent movements of the wrist and fingers. This study aims to classify and predict the results of the fitting and measured fingers and wrist cooperative actions using four commonly adopted classifiers and evaluate the performance of the classifiers in gesture fitting. The results indicated that linear discriminant analysis affords the highest classification performance, whereas the random forest method achieved the worst performance. This study can serve as a guide for gesture signal simplification in the future.

Automatic Detection of Noisy Signals in sEMG Grids Using Statistical Thresholding

Electromyogram (EMG) signal is often processed offline, after its acquisition, using digital signal processing algorithms to extract muscle anatomical and physiological information. As most of the signal processing algorithms work on an adequate quality of the signals, thus quality checking of the EMG in real-time during its acquisition is of immense importance. In multi-channel sEMG signals, usually there are some noisy or bad channels. If the noise is of low level, it is of little concern but high level of noise can limit the usefulness of the EMG. To make sure acquisition of a good quality EMG signal in terms of SNR, one way to detect noisy channels is through visual inspection by an expert human operator, however visual inspection of multiple electrodes in real-time is not possible and is also expensive both in terms of time and cost. In this research study, we propose a novel method for automatic detection of noisy channels in multi-channel surface EMG signals based on statistical thresholding of several parameters. The results of the proposed method are in perfect agreement with the ground truth for simulated EMG signals, with an accuracy of 98.6%.

A Novel PSO-Based Optimized Lightweight Convolution Neural Network for Movements Recognizing from Multichannel Surface Electromyogram

As the medium of human-computer interaction, it is crucial to correctly and quickly interpret the motion information of surface electromyography (sEMG). Deep learning can recognize a variety of sEMG actions by end-to-end training. However, most of the existing deep learning approaches have complex structures and numerous parameters, which make the network optimization problem difficult to realize. In this paper, a novel PSO-based optimized lightweight convolution neural network (PLCNN) is designed to improve the accuracy and optimize the model with applications in sEMG signal movement recognition. With the purpose of reducing the structural complexity of the deep neural network, the designed convolution neural network model is mainly composed of three convolution layers and two full connection layers. Meanwhile, the particle swarm optimization (PSO) is used to optimize hyperparameters and improve the autoadaptive ability of the designed sEMG pattern recognition model. To further indicate the potential application, three experiments are designed according to the progressive process of body movements with respect to the Ninapro standard data set. Experiment results demonstrate that the proposed PLCNN recognition method is superior to the four other popular classification methods.

Trbaggboost: an ensemble-based transfer learning method applied to Indian Sign Language recognition

An efficient sign language recognition (SLR) system would help speech and hearing-impaired people to communicate with normal people. This work aims to develop a SLR system for Indian sign language using data acquired from multichannel surface electromyogram, tri-axis accelerometers and tri-axis gyroscopes placed on both the forearms of signers. A novel ensemble-based transfer learning algorithm called Trbaggboost is proposed, which uses small amount of labeled data from a new subject along with labelled data from other subjects to train an ensemble of learners for predicting unlabeled data from the new subject. Conventional machine learning algorithms such as decision tree, support vector machine and random forest (RF) are used as base learners. The results for classification of signs using Trbaggboost are compared with commonly used transfer learning algorithms such as TrAdaboost, TrResampling, TrBagg, and simple bagging approach such as RF. Average accuracy for classification of signs performed by a new subject is achieved as 69.56% when RF is used without transfer learning. When just two observations of labeled data from a new subject are integrated with training data of an existing SLR system, average classification accuracy for TrAdaboost, TrResampling, TrBagg and RF are 71.07%, 72.92%, 76.10% and 76.79%, respectively. However, for the same number of labelled data from the new subject, Trbaggboost yields an average classification accuracy of 80.44%, indicating the effectiveness of the algorithm. Moreover, the classification accuracy for Trbaggboost improves up to 97.04% as the number of labelled data from the new user increase.

Spatio-temporal sEMG image enhancement and motor unit action potential (MUAP) detection: algorithms and their analysis

In spatiotemporal multi-channel surface electromyogram (EMG) images where the x-axis is time, the y-axis is EMG channels and the gray level is EMG amplitude, the motor unit action potential (MUAP) appears as a linear Gaussian structure. The appearance of this MUAP pattern in the spatiotemporal images is mostly distorted either by the destructive superposition of other MUAPs occurring in the conducting volume or by various noises such as a power line, bad electrode and skin contacts and movement artifacts. For accurate automatic detection of MUAP, EMG image enhancement is needed to suppress the background noises and enhance the line-like MUAP propagation patterns. This study presents several candidate filters to enhance the MUAPs propagation pattern in spatiotemporal EMG images. The filters, which can detect and enhance line-like structure in digital images, are used. Specifically, the Hermite shape filter is used for EMG image enhancement and compared with Gabor filter and steerable filters. The performance of the filters regarding accuracy, specificity, and sensitivity is evaluated with real sEMG signal measured from different muscles and computer-generated EMG signals. In the enhanced images the visibility of the MUAP region is improved. These results can help in better estimation of muscle characteristics from sEMG signals.

Finger Joint Angle Estimation Based on Motoneuron Discharge Activities.

Estimation of joint kinematics plays an important role in intuitive human-machine interactions. However, continuous and reliable estimation of small (e.g., the finger) joint angles is still a challenge. The objective of this study was to continuously estimate finger joint angles using populational motoneuron firing activities. Multi-channel surface electromyogram (sEMG) signals were obtained from the extensor digitorum communis muscles, while the subjects performed individual finger oscillatory extension movements at two different speeds. The individual finger movement was first classified based on the EMG signals. The discharge timings of individual motor units were extracted through high-density EMG decomposition, and were then pooled as a composite discharge train. The firing frequency of the populational motor unit firing events was used to represent the descending neural drive to the motor unit pool. A second-order polynomial regression was then performed to predict the measured metacarpophalangeal extension angle using the derived neural drive based on the neuronal firings. Our results showed that individual finger extension movement can be classified with >96% accuracy based on multi-channel EMG. The extension angles of individual fingers can be predicted continuously by the derived neural drive with R2 values >0.8. The performance of the neural-drive-based approach was superior to the conventional EMG-amplitude-based approach, especially during fast movements. These findings indicated that the neural-drive-based interface was a promising approach to reliably predict individual finger kinematics.

Analysis of end-plate using multi-channel surface EMG

By individually examining the characteristics of conducting waves in multi-channel surface electromyogram (EMG) signals, there is a possibility to understand the mobilization mechanism of muscle contraction. It is important to consider the potential obtained from the surface electromyogram (EMG) signal conducted from a large number of moving units in the muscle, as well as the interference potential due to the conducting action potential. The main purpose of this research paper is to find the exact characteristics of conducting waves close to an end-plate using the antiphase conduction method. To evaluate the position of the end-plate, every conducting wave will be investigated by multi-channel surface EMG, and the formation process for the conducting waves will be analyzed.

F59. Spasmodic reflex myoclonus-an characteristic electrophysiological manifestation for the diagnosis of Stiff-person syndrome

Introduction Stiff person syndrome (SPS) is a rare disease entity and the pivotal clinical features contain progressive course of fluctuating stiffness of the low back and lower limbs. Progressive encephalomyelitis with rigidity and myoclonus (PERM) is one of the unique variants of stiff person syndrome, and it was characterized with rigidity, stimulus-sensitive spasms, myoclonus, autonomic disturbance, and brainstem signs. These features resemble those of hyperekplexia. The accurate diagnosis of SPS is difficult but is crucial for the in time intervention with immunotherapy for life saving. In addition to the detection of responsible antibody, clinical electrophysiological manifestation, spasmodic reflex myoclonus, would be of great help in establishing the diagnosis. Methods Multichannel surface electromyogram (sEMG) and accelerometer recordings were conducted over the lumbar paraspinalis, rectus abdominalis, quadriceps, and hamstring muscles. Stimulus sensitive phenomenon was illustrated with accelerometer tapped over the pinprick. The signals were processed and digitized with CED power1401 with Spike2. The band pass for sEMG was set from 80 Hz to 1000 Hz. Results The results demonstrated that episodes of spontaneous onset of spasm of the low trunk and legs lasted about seconds to minutes. Stimuli with nose tip tapping, would elicit vigorous jerks with dissemination of long lasting muscle spasm of the neck, paraspinal muscle, rectus abdominalis muscle, to quadriceps and hamstring muscles in a descending spreading pattern. The unique electrophysiological manifestations illustrate the characteristic feature of spasmodic reflex myoclonus. The surface EMG recording also showed the phenomena of habituation, in which a trend of amplitude decrescendo from the first toward the third EMG responses were observed during consecutive auditory stimulus. The serum levels of anti-glutamic acid decarboxylase 65 antibodies was 164 IU. Conclusion The unique EMG pattern can help identify SPS. The clinical features of spasmodic reflex myoclonus are similar to hyperekplexia. However, there are some differences between the two phenomena. Unlike hyperekplexia, the spasmodic reflex myoclonus can be habituated after repetitive stimulation in the SPS patient. Furthermore, the initial myoclonic bursts of spasmodic reflex myoclonus were not present in hyperekplexia and the EMG burst duration was much shorter of the hyperekplexia as compared with that of spasmodic reflex myoclonus. These neurophysiological features discern spasmodic reflex myoclonus from hyperekplexia and are helpful in making diagnosis of SPS.

Spatial Analysis of Multichannel Surface EMG in Hemiplegic Stroke.

We investigated spatial activation patterns of upper extremity muscles during isometric force generation in both intact persons and in hemispheric stroke survivors. We used a 128-channel surface electromyogram (EMG) grid to record the electrical activity of biceps brachii muscles during these contractions. EMG data were processed to develop 2-D root mean square (RMS) maps of muscle activity. Our objective was to determine whether motor impairments following stroke were associated with changes in the muscle activity maps and in the spatial distribution of muscular activation. We found that, for a given subject, spatial patterns in muscle activity maps were consistent across all measured contraction levels differing only the RMS EMG. However, the maps from opposite arms (stroke-affected versus non-affected) of stroke survivors were significantly different from each other, especially when compared with the differences observed intact participants. Our analyses revealed that chronic stroke altered the size and location of the active region in these maps. The former is potentially related to disruption of fiber and tissue structure, possibly linked to factors such as extracellular fat accumulation, connective tissue infiltration, muscle fiber atrophy, fiber shortening, and fiber loss. Changes in spatial patterns in muscle activity maps may also be linked to a shift in the location of the innervation zone or the endplate region of muscles. Furthermore, the textural analysis of EMG activity maps showed a larger pixel-to-pixel variability in stroke-affected muscles. Alterations in the muscle activity maps were also related to functional impairment (estimated using Fugl-Meyer score) and to the degree of spasticity (estimated using the modified Ashworth scale). Overall, our investigation revealed that the muscle architecture and morphology were significantly altered in the chronic stroke.

A new optical flow model for motor unit conduction velocity estimation in multichannel surface EMG

Many studies have demonstrated the feasibility and benefits of Conduction Velocity (CV) estimation from surface electromyograms (EMGs) in various experimental conditions. Among them, a method based on optical flow was proposed recently, demonstrating relatively accurate CV estimation for EMG signals acquired in monopolar mode. We extended this method by a new data model that compensates more realistically for the spatial Motor Unit Action Potential (MUAP) shape variability and enables accurate CV estimation also in single-differential acquisition mode. The proposed modification was validated on 5000 synthetic Motor Units (MUs) with known CV and direction of fibres. It was shown that, in the noiseless case, the mean CV estimation error was significantly lower for our proposed modification compared to the original CV estimation procedure by up to 2% in the case of monopolar EMG signals and by up to 18.6% for single-differential EMG signals. When estimating fibre directions, the mean error was lower by up to 2.4° (for monopolar EMG signals) and 9.6° (for single-differential EMG signals). The results of tests with 10dB and 20dB noise further demonstrated the robustness of the proposed algorithm to noise in MUAP estimation.

Alterations in Neural Control of Constant Isometric Contraction with the Size of Error Feedback.

Discharge patterns from a population of motor units (MUs) were estimated with multi-channel surface electromyogram and signal processing techniques to investigate parametric differences in low-frequency force fluctuations, MU discharges, and force-discharge relation during static force-tracking with varying sizes of execution error presented via visual feedback. Fourteen healthy adults produced isometric force at 10% of maximal voluntary contraction through index abduction under three visual conditions that scaled execution errors with different amplification factors. Error-augmentation feedback that used a high amplification factor (HAF) to potentiate visualized error size resulted in higher sample entropy, mean frequency, ratio of high-frequency components, and spectral dispersion of force fluctuations than those of error-reducing feedback using a low amplification factor (LAF). In the HAF condition, MUs with relatively high recruitment thresholds in the dorsal interosseous muscle exhibited a larger coefficient of variation for inter-spike intervals and a greater spectral peak of the pooled MU coherence at 13–35 Hz than did those in the LAF condition. Manipulation of the size of error feedback altered the force-discharge relation, which was characterized with non-linear approaches such as mutual information and cross sample entropy. The association of force fluctuations and global discharge trace decreased with increasing error amplification factor. Our findings provide direct neurophysiological evidence that favors motor training using error-augmentation feedback. Amplification of the visualized error size of visual feedback could enrich force gradation strategies during static force-tracking, pertaining to selective increases in the discharge variability of higher-threshold MUs that receive greater common oscillatory inputs in the β-band.

A new approach for multi-channel surface EMG signal simulation.

Simulation models are necessary for testing the performance of newly developed approaches before they can be applied to interpreting experimental data, especially when biomedical signals such as surface electromyogram (SEMG) signals are involved. A new and easily implementable surface EMG simulation model was developed in this study to simulate multi-channel SEMG signals. A single fiber action potential (SFAP) is represented by the sum of three Gaussian functions. SFAP waveforms can be modified by adjusting the amplitude and bandwidth of the Gaussian functions. SEMG signals were successfully simulated at different detected locations. Effects of the fiber depth, electrode position and conduction velocity of SFAP on motor unit action potential (MUAP) were illustrated. Results demonstrate that the easily implementable SEMG simulation approach developed in this study can be used to effectively simulate SEMG signals.

Age-related changes in motor unit firing pattern of vastus lateralis muscle during low-moderate contraction.

Age-related changes in motor unit activation properties remain unclear for locomotor muscles such as quadriceps muscles, although these muscles are preferentially atrophied with aging and play important roles in daily living movements. The present study investigated and compared detailed motor unit firing characteristics for the vastus lateralis muscle during isometric contraction at low to moderate force levels in the elderly and young. Fourteen healthy elderly men and 15 healthy young men performed isometric ramp-up contraction to 70% of the maximal voluntary contractions (MVC) during knee extension. Multichannel surface electromyograms were recorded from the vastus lateralis muscle using a two-dimensional grid of 64 electrodes and decomposed with the convolution kernel compensation technique to extract individual motor units. Motor unit firing rates in the young were significantly higher (~+29.7%) than in the elderly (p<0.05). There were significant differences in firing rates among motor units with different recruitment thresholds at each force level in the young (p<0.05) but not in the elderly (p>0.05). Firing rates at 60% of the MVC force level for the motor units recruited at <20% of MVC were significantly correlated with MVC force in the elderly (r=0.885, p<0.0001) but not in the young (r=0.127, p>0.05). These results suggest that the motor unit firing rate in the vastus lateralis muscle is affected by aging and muscle strength in the elderly and/or age-related strength loss is related to motor unit firing/recruitment properties.

Conducting Wave Investigation by Multichannel Surface EMG

SUMMARYMuscular activity is investigated by analyzing the action potential generated by motor units. The multichannel surface electromyogram (EMG) is used to analyze the conduction of muscle potential, which is recorded as the interference electric potential generated by motor units. The multichannel surface EMG includes waveforms that conduct in several channels (conducting waves). We believe that it may be possible to analyze the changes in surface muscle potential caused by muscular activity so as to examine the composition of conducting waves in the multichannel surface EMG. Thus, in this paper, we propose a new method of analyzing the multichannel surface EMG. The proposed method involves quantitatively searching for all conducting waves that appear in the multichannel surface EMG in order to calculate the similarity of the waveforms. Using this method, which is referred to as the multichannel method for conducting waves (m‐ch method), various types of data for each wave, including the conduction velocity and amplitude, across channels can be examined. Using the m‐ch method, we analyzed the multichannel surface EMG in subjects holding a load. We also examined the changes in the composition of the conducting waves. We believe that muscular activity can be analyzed based on conducting waves from a surface EMG.

Three-Dimensional Innervation Zone Imaging from Multi-Channel Surface EMG Recordings.

There is an unmet need to accurately identify the locations of innervation zones (IZs) of spastic muscles, so as to guide botulinum toxin (BTX) injections for the best clinical outcome. A novel 3D IZ imaging (3DIZI) approach was developed by combining the bioelectrical source imaging and surface electromyogram (EMG) decomposition methods to image the 3D distribution of IZs in the target muscles. Surface IZ locations of motor units (MUs), identified from the bipolar map of their MU action potentials (MUAPs) were employed as a prior knowledge in the 3DIZI approach to improve its imaging accuracy. The performance of the 3DIZI approach was first optimized and evaluated via a series of designed computer simulations, and then validated with the intramuscular EMG data, together with simultaneously recorded 128-channel surface EMG data from the biceps of two subjects. Both simulation and experimental validation results demonstrate the high performance of the 3DIZI approach in accurately reconstructing the distributions of IZs and the dynamic propagation of internal muscle activities in the biceps from high-density surface EMG recordings.

Estimation of Lower Limb Periodic Motions from sEMG Using Least Squares Support Vector Regression

In this paper, a new technique for predicting human lower limb periodic motions from multi-channel surface ElectroMyoGram (sEMG) was proposed on the basis of least-squares support vector regression (LS-SVR). The sEMG signals were sampled from seven human lower limb muscles. Two channels sEMG were selected and mapped to muscle activation levels for angles estimation based on cross-correlation analysis. To deal with the time delay introduced by low-pass filtering of raw sEMG, a $$k$$k-order dynamic model was derived to represent the dynamic relationship between the joint angles and muscle activation levels. The dynamic model was built by data driven LS-SVR with radial basis function kernel. The inputs of the LS-SVR are muscle activation levels, and the outputs are joint angles of the hip and knee. In experiments, 48 sEMG-angle datasets sampled from six healthy people were utilized to verify the effectiveness of the proposed method. Result shows that the human lower limb joint angles can be well estimated in different motion conditions.

Spike sorting paradigm for classification of multi-channel recorded fasciculation potentials

BackgroundFasciculation potentials (FPs) are important in supporting the electrodiagnosis of Amyotrophic Lateral Sclerosis (ALS). If classified by shape, FPs can also be very informative for laboratory-based neurophysiological investigations of the motor units. MethodsThis study describes a Matlab program for classification of FPs recorded by multi-channel surface electromyogram (EMG) electrodes. The program applies Principal Component Analysis on a set of features recorded from all channels. Then, it registers unsupervised and supervised classification algorithms to sort the FP samples. Qualitative and quantitative evaluation of the results is provided for the operator to assess the outcome. The algorithm facilitates manual interactive modification of the results. Classification accuracy can be improved progressively until the user is satisfied. The program makes no assumptions regarding the occurrence times of the action potentials, in keeping with the rather sporadic and irregular nature of FP firings. ResultsTen sets of experimental data recorded from subjects with ALS using a 20-channel surface electrode array were tested. A total of 11891 FPs were detected and classified into a total of 235 prototype template waveforms. Evaluation and correction of classification outcome of such a dataset with over 6000 FPs can be achieved within 1–2 days. Facilitated interactive evaluation and modification could expedite the process of gaining accurate final results. ConclusionThe developed Matlab program is an efficient toolbox for classification of FPs.