Recorded Electromyogram Signals Research Articles

DECODING OF THE EXTRAOCULAR MUSCLES ACTIVATIONS BY COMPLEXITY-BASED ANALYSIS OF ELECTROMYOGRAM (EMG) SIGNALS

The analysis of extraocular muscles’ activation is crucial for understanding eye movement patterns, providing insights into oculomotor control, and contributing to advancements in fields such as vision research, neurology, and biomedical engineering. Ten subjects went through the experiments, including normal watching, blinking, upward and downward movements of eyes, and eye movements to the left and right while their electromyogram (EMG) signals were recorded. We analyzed the complexity of recorded EMG signals using fractal theory, sample entropy, and approximate entropy (ApEn). The results showed that the techniques are able to decode the changes in the complexity of EMG signals between different eye movements. In other words, we can use these methods to study extraocular muscle activations in different conditions.

A Scale Mixture-Based Stochastic Model of Surface EMG Signals With Variable Variances.

Surface electromyogram (EMG) signals have typically been assumed to follow a Gaussian distribution. However, the presence of non-Gaussian signals associated with muscle activity has been reported in recent studies, and there is no general model of the distribution of EMG signals that can explain both non-Gaussian and Gaussian distributions within a unified scheme. In this paper, we describe the formulation of a non-Gaussian EMG model based on a scale mixture distribution. In the model, an EMG signal at a certain time follows a Gaussian distribution, and its variance is handled as a random variable that follows an inverse gamma distribution. Accordingly, the probability distribution of EMG signals is assumed to be a mixture of Gaussians with the same mean but different variances. The EMG variance distribution is estimated via marginal likelihood maximization. Experiments involving nine participants revealed that the proposed model provides a better fit to recorded EMG signals than conventional EMG models. It was also shown that variance distribution parameters may reflect underlying motor unit activity. This study proposed a scale mixture distribution-based stochastic EMG model capable of representing changes in non-Gaussianity associated with muscle activity. A series of experiments demonstrated the validity of the model and highlighted the relationship between the variance distribution and muscle force. The proposed model helps to clarify conventional wisdom regarding the probability distribution of surface EMG signals within a unified scheme.

Estimation of Upper Limb Joint Angle Using Surface EMG Signal

In the development of robot-assisted rehabilitation systems for upper limb rehabilitation therapy, human electromyogram (EMG) is widely used due to its ability to detect the user intended motion. EMG is one kind of biological signal that can be recorded to evaluate the performance of skeletal muscles by means of a sensor electrode. Based on recorded EMG signals, user intended motion could be extracted via estimation of joint torque, force or angle. Therefore, this estimation becomes one of the most important factors to achieve accurate user intended motion. In this paper, an upper limb joint angle estimation methodology is proposed. A back propagation neural network (BPNN) is developed to estimate the shoulder and elbow joint angles from the recorded EMG signals. A Virtual Human Model (VHM) is also developed and integrated with BPNN to perform the simulation of the estimated angle. The relationships between sEMG signals and upper limb movements are observed in this paper. The effectiveness of our developments is evaluated with four healthy subjects and a VHM simulation. The results show that the methodology can be used in the estimation of joint angles based on EMG.

Influence of motor unit synchronization on amplitude characteristics of surface and intramuscularly recorded EMG signals

The increase in muscle strength without noticeable hypertrophic adaptations is very important in some sports. Motor unit (MU) synchronisation and higher rate of MU activation are proposed as possible mechanisms for such a strength and electromyogram (EMG) increase in the early phase of a training regimen. Root mean square and/or integrated EMG are amplitude measures commonly used to estimate the adaptive changes in efferent neural drive. EMG amplitude characteristics could change also because of alteration in intracellular action potential (IAP) spatial profile. We simulated MUs synchronization under different length of the IAP profile. Different synchronization was simulated by variation of the percent of discharges in a referent MU, to which a variable percent of remaining MUs was synchronized. Population synchrony index estimated the degree of MU synchronization in EMG signals. We demonstrate that the increase in amplitude characteristics due to MU synchronization is stronger in surface than in intramuscularly detected EMG signals. However, the effect of IAP profile lengthening on surface detected EMG signals could be much stronger than that of MU synchronization. Thus, changes in amplitude characteristics of surface detected EMG signals with progressive strength training could hardly be used as an indicator of changes in neural drive without testing possible changes in IAPs.

Simulation of Surface EMG Signals for a Multilayer Volume Conductor With a Superficial Bone or Blood Vessel

This study analytically describes surface electromyogram (EMG) signals generated by a planar multilayer volume conductor constituted by different subdomains modeling muscle, bone (or blood vessel), fat, and skin tissues. The bone is cylindrical in shape, with a semicircular section. The flat portion of the boundary of the bone subdomain is interfaced with the fat layer tissue, the remaining part of the boundary is in contact with the muscle layer. The volume conductor is a model of physiological tissues in which the bone is superficial, as in the case of the tibia bone, backbone, and bones of the forearm. The muscle fibers are considered parallel to the axes of the bone, so that the model is space invariant in the direction of propagation of the action potential. The proposed model, being analytical, allows faster simulations of surface EMG with respect to previously developed models including bone or blood vessels based on the finite-element method. Surface EMG signals are studied by simulating a library of single-fiber action potentials (SFAP) of fibers in different locations within the muscle domain, simulating the generation, propagation, and extinction of the action potential. The decay of the amplitude of the SFAPs in the direction transversal to the fibers is assessed. The decay in the direction of the bone has a lower rate with respect to the opposite direction. Similar results are obtained by simulating motor unit action potentials (MUAPs) constituted by 100 fibers with territory 5 mm2. M waves and interference EMG signals are also simulated based on the library of SFAPs. Again, the decay of the amplitude of the simulated interference EMG signals is lower approaching the bone with respect to going farther from it. The findings of this study indicate the effect of a superficial bone in enhancing the EMG signals in the transversal direction with respect to the fibers of the considered muscle. This increases the effect of crosstalk. The same mathematical method used to simulate a superficial bone can be applied to simulate other physiological tissues. For example, superficial blood vessels (e.g., basilic vein, brachial artery) can influence the recorded EMG signals. As the electrical conductivity of blood is high (it is of the same order as the longitudinal conductivity in the muscle), the effect on EMG signals is opposite compared to the effect of a superficial bone.