Evaluation of Capacitive EMG Sensor Geometries by Simulation and Measurement

Electrical stimulation of peripheral nerves can elicit sensations in the residual and missing limbs of amputees. The goal of this study is to determine how electrical stimulation activates peripheral neural pathways by examining the relationship between elicited sensations and activation of residual muscles proximal to the amputation. In this study, high-density (16-contact) composite flat interface nerve electrodes were implanted on the sciatic and tibial nerves above the knee of a participant with a trans-tibial amputation. The sensations elicited by applying stimulating pulses to selected contacts within the electrodes were classified into three categories: tactile sensation in the residual limb, tactile sensation in the missing limb, and feeling of muscle contraction in the residual limb. Surface electromyograms (EMG) were recorded from below-knee muscles in the residual limb prior to and during stimulation. Initial results suggest direct peripheral afferent activation induces tactile sensation in the participant’s missing limb. No significant elevated EMG was observed in any of the below-knee muscles when the subject perceived tactile sensation in his missing limb. However, EMG activity was observed in at least one of the muscles when he perceived tactile sensation in his residual limb. Sensations of muscle contractions coincided with a much higher EMG activity in all residual muscles. Our findings suggest that elicited tactile sensations in the missing limb are largely due to direct activation of afferent fibers. However, muscle contraction sensation in the residual limb may result from afferent activation by muscle recruitment.

Identification of the innervation zone is widely used to optimize the accuracy and precision of noninvasive surface electromyography (EMG) signals because the EMG signal is strongly influenced by innervation zones. However, simply structured fusiform muscle, such as biceps brachii muscle, has been employed mainly due to the simplicity with which the propagation from raw EMG signals can be observed. In this study, the optimum electrode location (OEL), free from innervational influence, was investigated by the propagation pattern of action potentials for brachii muscles and more complicated deltoid muscle structures using an automatized signal analysis technique. The technique employed newly developed computer software with additional clinical uses and minimized subjective differences. EMG signals were recorded using surface array electrodes during voluntary isometric contractions obtained from 12 healthy male subjects. Peaks in EMG signals were detected and averaged for each muscle. The propagation patterns and OEL were examined from biceps brachii muscles for all subjects and from deltoid muscles for seven subjects. The estimated locations were partially confirmed by comparing the root mean squares of the EMG signals. These results show that propagation patterns and OEL could be estimated simply and automatically even from the surface EMG signals of deltoid muscles.

With the advent of various techniques to assess the bioelectric signals on the surface of the body, it has become possible to develop various human-computer interface systems. In this study, for the first-time a cross- correlation based data is reported for two-different types of bio-signals viz. EEG (Electroencephalography) and EMG (Electromyography). Whereas EEG refers to the neuro-electric impulses generated in the brain recorded in the form of electric potentials, EMG records the activation potentials of the muscle cells when they contract or relax. The ability of Hindustani Music (HM) to evoke a wide range of emotional experience in its listeners is widely known. For this study, we took simultaneous EEG and EMG data of five participants while they listened to two Hindustani ragas of contrast emotions namely Chayanat (corresponding to happy/joy) and Darbari Kanada (corresponding to sad/pathos) emotion. We made use of two latest signal processing algorithms, wavelet-based power spectra and cross-correlation coefficient to assess the arousal-based activities in response to the acoustic clips in the two different bio-signals. For the first time, an attempt is being made to quantify and categorize musical emotions using EMG signal and an attempt to correlate that with the EEG signals obtained from brain. The alpha, theta and gamma frequency range in the frontal and parietal electrodes is found to be the most responsive in case of musical emotions. The EMG response has been studied by segregating the entire signal into different frequency ranges as is done in case of EEG frequency bands. Interestingly, the response in case of EMG data is strongest in the same frequency bands as that of EEG signals. Novel pitch detection algorithm has also been applied to EMG signals to ratify the rationale behind the separation of frequency bands. This is the first of its kind study which looks for categorization and quantification of musical emotions using simultaneously two different bio-signals with the help of robust mathematical analysis. The results and implications have been discussed in detail. Keywords : EEG, EMG, Hindustani classical music, musical emotion, wavelet transform Cite this Article Uddalok Sarkar, Soumyadeep Pal, Sayan Nag, Shankha Sanyal, Archi Banerjee, Souparno Roy, Ranjan Sengupta, Dipak Ghosh. Music on Mind and Body: A Simultaneous EEG and EMG Study to Quantify Emotions from Hindustani Classical Music. Research and Reviews: Journal of Neuroscience . 2019; 9(2): 27–38p.

This paper proposes to monitor and recognize the fatigue state during football training by analyzing the surface electromyography (EMG) signals. The surface electromyography (EMG) signal is closely connected with the state during sports and training. First, power frequency interference, motion artifacts, and baseline drift in the surface electromyography (EMG) signal are removed; second, the authors extract 6 features: rectified average value (ARV), integrated electromyography myoelectric value (IEMG), root mean square of electromyography value (RMS), median frequency (MF), average power frequency (MPF), and electromyography power (TP) to represent the surface electromyography (EMG) signal; lastly, the extracted features are input into a one-class support vector machine to determine whether the player has been fatigued and are input into a weighted support vector machine to determine the degree of fatigue if the player has been fatigued. The experimental results show that more than 95% of the fatigue state can be recognized by surface electromyography (EMG) signal.

Control of transhumeral prosthetic devices can effectively be performed using the predicted joint torques at the elbow. The joint torque values are generally predicted using the Electromyography (EMG) signals taken from upper arm muscles of the amputee. This paper uses a Bagnoli-16 EMG system to extract EMG signals from the biceps and triceps. The EMG signals are complex to handle mainly due to the stochastic nature of the signal. Independent component analysis (ICA) is utilized to isolate the EMG signals from each muscle. In order to measure the actual torque, a novel kinematic model is proposed in this paper. For the joint torque prediction two classifiers have been developed. First an Artificial Neural Network model (ANN) based classifier is trained to predict the joint torques. Using different test data the ANN model is tested against the arm kinematic based joint torque predictions. The test results indicated 5.6% of root mean square error against the actual predicted torque values. In order to improve the classification an Artificial Neuro-Fuzzy inference system (ANFIS) has been developed. Using the same data the ANFIS based classifier produced 3.3% of the root mean square error against the kinematically predicted joint torques.

Surface Electromyography (EMG) is a standard method used in clinical practice and research to assess motor function in order to help with the diagnosis of neuromuscular pathology in human and animal models. EMG recorded from trunk muscles involved in the activity of breathing can be used as a direct measure of respiratory motor function in patients with spinal cord injury (SCI) or other disorders associated with motor control deficits. However, EMG potentials recorded from these muscles are often contaminated with heart-induced electrocardiographic (ECG) signals. Elimination of these artifacts plays a critical role in the precise measure of the respiratory muscle electrical activity. This study was undertaken to find an optimal approach to eliminate the ECG artifacts from EMG recordings. Conventional global filtering can be used to decrease the ECG-induced artifact. However, this method can alter the EMG signal and changes physiologically relevant information. We hypothesize that, unlike global filtering, localized removal of ECG artifacts will not change the original EMG signals. We develop an approach to remove the ECG artifacts without altering the amplitude and frequency components of the EMG signal by using an externally recorded ECG signal as a mask to locate areas of the ECG spikes within EMG data. These segments containing ECG spikes were decomposed into 128 sub-wavelets by a custom-scaled Morlet Wavelet Transform. The ECG-related sub-wavelets at the ECG spike location were removed and a de-noised EMG signal was reconstructed. Validity of the proposed method was proven using mathematical simulated synthetic signals and EMG obtained from SCI patients. We compare the Root-mean Square Error and the Relative Change in Variance between this method, global, notch and adaptive filters. The results show that the localized wavelet-based filtering has the benefit of not introducing error in the native EMG signal and accurately removing ECG artifacts from EMG signals.

Relationships of Vibromyographic and Electromyographic Signals During Isometric Voluntary Contraction

The control of multifunctional myoelectric prostheses is usually limited due to insufficient electromyography (EMG) signals, especially for individuals after above-elbow amputations. Targeted muscle reinnervation (TMR) is a method to recover the lost EMG sources, where the nerves associated with residual limbs are transferred onto targeted muscles to enable the recording of EMG signals that are sufficient for prosthesis control. Following TMR surgeries, the implanted nerves would require a relatively long time to grow during which the targeted muscles may atrophy due to denervation. To treat and accelerate the nerve growth, functional electrical stimulation (FES) has been proved to be an effective method. In this study, we investigated the influence of FES on targeted muscles and nerve rehabilitation after TMR surgery, based on rat models. Specifically, twelve Sprague Dawley adult male rats underwent TMR surgeries were equally divided into a control group and an experimental group, where only the experimental group was treated with FES. The results received four weeks post the TMR surgery indicate that both the targeted muscle preservation rate and the maximal contraction of the experimental group were significantly higher than those of the control group. This preliminary result suggests that FES treatment could improve the rehabilitation procedure for targeted muscles and implanted nerves of rats after TMR surgeries, which would potentially facilitate future clinic applications of TMR technique.

Objective: To investigate the characteristics of electromyography (EMG) signals and the starting threshold voltages of the orbicularis oris muscles (OOM) in healthy rhesus monkeys under different muscle movement conditions. Methods: The EMG signals and the starting threshold voltages at different time points in 4 healthy rhesus monkeys were acquired and recorded with EMG device and evoked potentiometer. The voltage amplitude variation of EMG signals was analyzed, and the voltage amplitude range of EMG signals at the beginning of OOM contraction was established. The data were statistically analyzed by one-way ANOVA. Results: The EMG of OOM in healthy monkeys in the quiet, natural and continuous mouth-closed state was linear and relatively stable, and the absolute value fluctuated between 15 and 50 μV. The EMG waveform increased rapidly during the natural lip contraction movement, and its amplitude fluctuated greatly, with the highest absolute value of the peak value reaching hundreds of microvolts. The amplitude of EMG induced by continuous mouth closure was more than thousands of microvolts. There was no significant difference in EMG amplitudes of OOM in the healthy rhesus monkey under quiet and continuous lip closure at different time points (P>0.05). There was no significant difference in threshold voltages in the state of natural lip contraction of bilateral OOM at different time points (average range: 57.17-57.47 μV) in the healthy rhesus monkeys (P>0.05). There was no significant difference in threshold voltages of OOM induced by bilateral OOM at different time points(average range: 55.38-55.99 μV) in the healthy rhesus monkeys(P>0.05). There were significant differences in the absolute values of EMG amplitudes of OOM between the three lip movement modes: (30.67±8.72) μV in quiet and natural continuous lip closure (475.12±54.72) μV in natural lip contraction, and (921.22±312.79) μV in the induced persistent lip closure, with t values of -8.48, -9.35 and -5.01 respectively, all P<0.001. Conclusions: The EMG signals of OOM show different characteristics under different muscle movement conditions, which can be used as a basis for computer to judge and recognize the movement conditions of OOM. The upper limits of the EMG threshold voltage values of OOM under different motion states are 55-60 μV.

As the development of those Electromyography assisted biodynamic models, the requirements of Electromyography (EMG) signals have become increasingly important. Traditionally, subjects always need whenever experiment need to put into practice. Therefore, the requirements of EMG simulation increased significantly. The traditional method for the simulation of EMG is based on the physiological mechanisms of muscle contraction. So, it is very important to deeply understand the physiological mechanisms of muscle contraction. Actually, this mechanism is very complicated, and it is usually very hard to simulation the specific EMG signals for a specific subject. A new technique was developed. It is based on the experiment data and eliminates the need to collect EMG signals by electrode. This new technique demonstrated at least as similar signal shapes and similar model fidelity when compared to the traditional technique, indicating that it is a valid alternative to traditional method. The subjects who were once joined the experiment and the EMG collection systems are no longer needed. In particular, this technique will be valuable for evaluating some exertion once can't be taken, or can't be finished by the specific subject. In addition, based on those already existing subjects, it is very easy to set the relevant properties of specific subject, and then generate virtual subject and the simulated EMG signal according to any simulation conditions.

An automated system for resolving an intramuscular electromyographic (EMG) signal into its constituent motor unit potential trains (MUPTs) is presented. The system is intended mainly for clinical applications where several physiological parameters for each motor unit (MU), such as the motor unit potential (MUP) template and mean firing rate, are required. The system decomposes an EMG signal off-line by filtering the signal, detecting MUPs, and then grouping the detected MUPs using a clustering and a supervised classification algorithm. Both the clustering and supervised classification algorithms use MUP shape and MU firing pattern information to group MUPs into several MUPTs. Clustering is partially based on the K-means clustering algorithm. Supervised classification is implemented using a certainty-based classifier technique that employs a knowledge-based system to merge trains, detect and correct invalid trains, as well as adjust the assignment threshold for each train. The accuracy (93.2%±5.5%), assignment rate (93.9%±2.6%), and error in estimating the number of MUPTs (0.3±0.5) achieved for 10 simulated EMG signals comprised of 3-11 MUPTs are encouraging for using the system for decomposing various EMG signals.

The study and measurement of the magnetic field from the skeletal muscle is called Magnetomyography (MMG). These magnetic fields are produced by the same ion currents which give rise to the electrical signals that are recorded with electromyography (EMG). For non-invasive measurements, the electric properties of subcutaneous tissue, i.e., most importantly, have a strong influence on the recorded signals. This paper presents a computational model to study the volume conduction effect with the finite-difference time-domain simulations using Sim4Life. The effects of 1 mm fat on the recorded electrical and magnetic signals from the skin surface have been evaluated in both EMG and MMG. The results indicate that due to 1 mm fat, the electrical signals decrease over 60% through traveling across layers between the muscle and skin surface, while these layers are transparent to the magnetic field. In a similar simulation procedure, when the new fibers are recruited, the interference among electrical signals makes the strength of recorded signals behave non-linearly proportional to the increasing number of active muscle fibers. Sim4Life simulations show that the recorded magnetic signals do not have the same trajectory as electrical signals. Hence, the changes in EMG signals caused by volume conduction effect can result in signal misinterpretations.

This paper is motivated by works done in the area of robot-assisted stroke rehabilitation. The use of electromyographic (EMG) signal brings a new way of communication interface between user and robot. However, the EMG signal has to be transferred into useful information that serve as robot input. This paper presents a novel methodology for conversion of electromyographic (EMG) signal into estimated joint torque. Investigation of the proposed methodology covers human upper limb movement: shoulder flexion-extension, shoulder abduction-adduction, and elbow flexion-extension. Simulated annealing (SA) is implemented to obtain optimum model that maps EMG into estimated joint torque. General principle, design, and the implementation of SA for the problem are discussed in this paper. Experimentation was carried out to investigate the feasibility of the proposed algorithm. The results show that the algorithm is able to find optimum model that enables EMG to joint torque conversion.

It is well known that patients with a pseudocholinesterase (PChE) deficiency will have an initial negative electromyography (EMG) signal during parathyroid surgery. However, the time to return to normal EMG signals in a patient with PChE deficiency who received succinylcholine combined with the common obstacles to EMG monitoring is yet to be described in the literature. Here we present the diagnostic challenges in a patient with unknown PChE deficiency undergoing parathyroidectomy. The patient was a 74-year-old female and presented for elective parathyroidectomy with intraoperative neuromonitoring. The anesthesia induction was performed with propofol, fentanyl and succinylcholine. During the case, EMG responses were present; however, upon emergence, the patient was noted to exhibit profound weakness. Train-of-four ratio (TOFR) was 0.8, and post tetanic potentiation fade was recognized. Sedation and ventilation continued until the patient regained sustained tetanus with twitch monitor stimulation. Follow-up PChE levels were later evaluated, and her level was found to be 952 U/L, with normal being 2,900-7,100 U/L, which is consistent with homozygous PChE deficiency. If the EMG signal is missing in the early phase or if the EMG amplitude increases over time, suspicion for PChE deficiency should be high. Reviewing the literature, we recognized that there are no comprehensive checklists for EMG signals during parathyroid surgery. Based on our case report, we recommend establishing evidence-based anesthesia checklists at institutions that perform EMG monitoring since false negative results can have safety and economic implications.

Objective. The identification of common oscillatory inputs to motor neurons in the electromyographic (EMG) signal power spectrum is often preceded by EMG rectification for enhancing the low-frequency oscillatory components. However, rectification is a nonlinear operator and its influence on the EMG signal spectrum is not fully understood. In this study, we aim at determining when EMG rectification is beneficial in the study of oscillatory inputs to motor neurons. Approach. We provide a full mathematical description of the power spectrum of the rectified EMG signal and the influence of the average shape of the motor unit action potentials on it. We also provide a validation of these theoretical results with both simulated and experimental EMG signals. Main results. Simulations using an advanced computational model and experimental results demonstrated the accuracy of the theoretical derivations on the effect of rectification on the EMG spectrum. These derivations proved that rectification is beneficial when assessing the strength of low-frequency (delta and alpha bands) common synaptic inputs to the motor neurons, when the duration of the action potentials is short, and when the level of cancellation is relatively low. On the other hand, rectification may distort the estimation of common synaptic inputs when studying higher frequencies (beta and gamma), in a way dependent on the duration of the action potentials, and may introduce peaks in the coherence function that do not correspond to physiological shared inputs. Significance. This study clarifies the conditions when rectifying the surface EMG is appropriate for studying neural connectivity.