Features Of Electromyographic Signals Research Articles

Anatomically accurate model of EMG during index finger flexion and abduction derived from diffusion tensor imaging.

This study presents a modelling framework in which information on muscle fiber direction and orientation during contraction is derived from diffusion tensor imaging (DTI) and incorporated in a computational model of the surface electromyographic (EMG) signal. The proposed model makes use of the principle of reciprocity to simultaneously calculate the electric potentials produced at the recording electrode by charges distributed along an arbitrary number of muscle fibers within the muscle, allowing for a computationally efficient evaluation of extracellular motor unit action potentials. The approach is applied to the complex architecture of the first dorsal interosseous (FDI) muscle of the hand to simulate EMG during index finger flexion and abduction. Using diffusion tensor imaging methods, the results show how muscle fiber orientation and curvature in this intrinsic hand muscle change during flexion and abduction. Incorporation of anatomically accurate muscle architecture and other hand tissue morphologies enables the model to capture variations in extracellular action potential waveform shape across the motor unit population and to predict experimentally observed differences in EMG signal features when switching from index finger abduction to flexion. The simulation results illustrate how structural and electrical properties of the tissues comprising the volume conductor, in combination with fiber direction and curvature, shape the detected action potentials. Using the model, the relative contribution of motor units of different sizes located throughout the muscle under both conditions is examined, yielding a prediction of the detection profile of the surface EMG electrode array over the muscle cross-section.

Proof of Concept of an Online EMG-Based Decoding of Hand Postures and Individual Digit Forces for Prosthetic Hand Control.

Options currently available to individuals with upper limb loss range from prosthetic hands that can perform many movements, but require more cognitive effort to control, to simpler terminal devices with limited functional abilities. We attempted to address this issue by designing a myoelectric control system to modulate prosthetic hand posture and digit force distribution. We recorded surface electromyographic (EMG) signals from five forearm muscles in eight able-bodied subjects while they modulated hand posture and the flexion force distribution of individual fingers. We used a support vector machine (SVM) and a random forest regression (RFR) to map EMG signal features to hand posture and individual digit forces, respectively. After training, subjects performed grasping tasks and hand gestures while a computer program computed and displayed online feedback of all digit forces, in which digits were flexed, and the magnitude of contact forces. We also used a commercially available prosthetic hand, the i-Limb (Touch Bionics), to provide a practical demonstration of the proposed approach's ability to control hand posture and finger forces. Subjects could control hand pose and force distribution across the fingers during online testing. Decoding success rates ranged from 60% (index finger pointing) to 83-99% for 2-digit grasp and resting state, respectively. Subjects could also modulate finger force distribution. This work provides a proof of concept for the application of SVM and RFR for online control of hand posture and finger force distribution, respectively. Our approach has potential applications for enabling in-hand manipulation with a prosthetic hand.

Extraction of EMG signal in a software compatible format from an online database using WFDB package

Summary The electromyography (EMG) is an electro diagnostic procedure for evaluating the electrical activity of skeletal muscles. It is very difficult to deduce the structure and activation of surrounding muscle tissue from needle-detected EMG signals and thus the extraction of information regarding presence, absence or severity of disease is not possible. The muscle morphology, activation and similarity with measured electromyographic signal features can be explored by comparison with simulated EMG signal. Evaluation of the data using such reference model will provide better understanding of the properties of muscles and their relationship to EMG signals. As the difficulty lies in the non-availability of standard EMG signal, the present work thus focuses on MATLAB implementation of needle detected EMG model introduced by Hamilton-Wright and Stashuk in 2005. The first step of analysis involves conversion of raw EMG signal from .dat format to .txt/.mat format which is achieved by using WFDB (Waveform Database) Software Package. In this research work standard simulated signals available in S002 dataset ( Hamilton-Wright and Stashuk, 2005 ) are extracted and the proposed procedure is presented in a detailed manner.

User adaptation in long-term, open-loop myoelectric training: implications for EMG pattern recognition in prosthesis control

Objective. Recent studies have reported that the classification performance of electromyographic (EMG) signals degrades over time without proper classification retraining. This problem is relevant for the applications of EMG pattern recognition in the control of active prostheses. Approach. In this study we investigated the changes in EMG classification performance over 11 consecutive days in eight able-bodied subjects and two amputees. Main results. It was observed that, when the classifier was trained on data from one day and tested on data from the following day, the classification error decreased exponentially but plateaued after four days for able-bodied subjects and six to nine days for amputees. The between-day performance became gradually closer to the corresponding within-day performance. Significance. These results indicate that the relative changes in EMG signal features over time become progressively smaller when the number of days during which the subjects perform the pre-defined motions are increased. The performance of the motor tasks is thus more consistent over time, resulting in more repeatable EMG patterns, even if the subjects do not have any external feedback on their performance. The learning curves for both able-bodied subjects and subjects with limb deficiencies could be modeled as an exponential function. These results provide important insights into the user adaptation characteristics during practical long-term myoelectric control applications, with implications for the design of an adaptive pattern recognition system.

Surface EMG Signal Amplification and Filtering

Electromyographic (EMG) signals have been widely employed as a control signal in rehabilitation and a means of diagnosis in health care. Signal amplification and filtering is the first step in surface EMG signal processing and application systems. The characteristics of the amplifiers and filters determine the quality of EMG signals. Up until now, searching for better amplification and filtering circuit design that is able to accurately capture the features of surface EMG signals for the intended applications is still a challenging. With the fast development in computer sciences and technologies, EMG signals are expected to be used and integrated within small or even tiny intelligent, automatic, robotic, and mechatronics systems. This research focused on small size amplification and filtering circuit design for processing surface EMG signals from an upper limb and aimed to fix the amplifiers and filters inside a robotic hand with limited space to command and control the robot hand movement. The research made a study on the commonly used methodologies for EMG signal processing and circuitry design and proposed a circuit design for EMG signal amplification and filtering. High-pass filters including secondorder and fourth-order with the suppression to low frequency noises are studied. The analysis, verification and experiment showed that a second-order high-pass filter can adequately suppress the low frequency noises. The proposed amplification and filtering circuit design is able to effectively clean the noises and collect the useful surface EMG signals from an upper limb. The experiment also clearly revealed that power line interference needs to be carefully handled for higher signalnoise-ratio (SNR) as a notch-filter might cause the loss of useful signal components. Commercial computer software such as LabView and Matlab were used for data acquisition software development and data analysis. General Terms EMG data acquisition, Bio-signal processing, Bio-informatics

Research on the electromyographic signals of orbicularis oculi muscle of rabbit

To study the features of electromyographic signals of orbicularis oculi muscle (OOM) of normal rabbits in various movement states, and to clarify relationships between functional actions of OOM and their electromyographic signals, hoping to obtain information concerning the electromyographic signals controlling OOM as reference for restoring the eye-closing function by artificial facial nerve prosthesis in patients with unilateral peripheral facial palsy. The electromyographic signals were extracted from OOM of normal rabbits by implanted microelectrodes through upper and lower eyelids. Then the features of these electromyographic signals were analyzed in the time domain and the frequency domain. The peak values of the absolute electromyographic amplitude for natural continuous eye-opening event, natural continuous eye-closing state, natural eye-blinking movement and evoked eye-closing state were (28.8 ± 4.8) µV, (36.0 ± 4.7) µV, (398.8 ± 195.7) µV, and (715.4 ± 249.7) µV, respectively. The peak frequency values of the power spectrum density (PSD) of electromyographic signals for the four modes were (98 ± 17) Hz, (142 ± 22) Hz, (203 ± 58) Hz, and (349 ± 81) Hz, respectively. The electrical activities during the natural continuous eye-opening event and the natural continuous eye-closing state were stable and displayed low amplitudes. During the spontaneous blink state and the evoked eye-closing state, the electromyographic amplitudes markedly increased, and the increased level in the latter state was stronger than that in the former state. When rabbits continuously closed eyes or opened eyes, all of the peak values of the absolute voltage amplitudes were less than 50 µV. The absolute amplitude values of the starting site were between 50 µV and 60 µV during the spontaneous blink and the evoked eye-closing movements. The whole frequency band of the energy of PSD about OOM was between 0 Hz and 500 Hz, and the focus frequency range was between 20 Hz and 350 Hz.In the time domain, the difference was not significant for the electromyographic signals of OOM between the continuous eye-opening state and the continuous eye-closing movement (P > 0.05), but there were statistically significant differences in the other states for their pairwise comparisons (P < 0.05). In the frequency domain, there was no statistically significant difference for the peak frequency of PSD about the electromyographic signals when comparing the continuous eye-opening state with the continuous eye-closing event (P > 0.05). When comparing this item in the other movements with each other, however, the differences were statistically significant (P < 0.05). OOM relaxes when eyelid keeps continuously opening. The action of eyelid-closing is due to contraction of this muscle. Each state has its own features of the electromyographic signals for OOM, these features can be used as criteria for computers to judge and identify various movement states of OOM. However, it is difficult to distinguish the natural continuous eye-opening event from the natural continuous eye-closing state, based on the features of electromyographic signals in the time and frequency domain.

Terrain Identification for Prosthetic Knees Based on Electromyographic Signal Features

The features of electromyographic (EMG) signals were investigated while people walking on different terrains, including up and down slopes, up and down stairs, and during level walking at different speeds. The features were used to develop a terrain identification method. The technology can be used to develop an intelligent transfemoral prosthetic limb with terrain identification capability. The EMG signals from 8 hip muscles of 13 healthy persons were recorded as they walked on the different terrains. The signals from the sound side of a transfemoral amputee were also recorded. The features of these signals were obtained using data processing techniques with an identification process developed for the identification of the terrain type. The procedure was simplified by using only the signals from three muscles. The identification process worked well in an intelligent prosthetic knee in a laboratory setting.

Physiologically Based Simulation of Clinical EMG Signals

An algorithm that generates electromyographic (EMG) signals consistent with those acquired in a clinical setting is described. Signals are generated using a model constructed to closely resemble the physiology and morphology of skeletal muscle, combined with line source models of commonly used needle electrodes positioned in a way consistent with clinical studies. The validity of the simulation routines is demonstrated by comparing values of statistics calculated from simulated signals with those from clinical EMG studies of normal subjects. The simulated EMG signals may be used to explore the relationships between muscle structure and activation and clinically acquired EMG signals. The effects of motor unit (MU) morphology, activation, and neuromuscular junction activity on acquired signals can be analyzed at the fiber, MU and muscle level. Relationships between quantitative features of EMG signals and muscle structure and activation are discussed.