Investigation of optimum electrode locations by using an automatized surface electromyography analysis technique.

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.     

Analysis and comparison of reflex times and electromyograms of cervical muscles under impact loading using surface and fine-wire electroces

Myoelectric signals [electromyograms (EMGs)] can be collected using either surface or fine-wire electrodes. Application of the latter results in higher-frequency contents of EMG. In the field of impact biomechanics, surface electrodes are more often utilized than fine-wire ones. However, the removal of motion artefacts from EMG recorded under transient loads requires application of high-pass filters with relatively high cutoff frequencies, which may eliminate a significant part of the surface EMG power spectra. Therefore, in the current study, both surface and fine-wire electrodes were utilized to record the EMG of cervical muscles under conditions simulating a rear-end car collision at low speed. The results indicated that application of high-pass filtering at 50 Hz can be necessary to remove motion artefacts from the EMG collected under such conditions. Such filtering resulted in a higher decrease in amplitude of the surface EMG than that of the fine-wire one. However, the reflex times obtained here were not significantly affected by the type of the electrodes utilized to collect EMG.     

Selectivity of spatial filters for surface EMG detection from the tibialis anterior muscle

Many spatial filters have been proposed for surface electromyographic (EMG) signal detection. Although theoretical and modeling predictions on spatial selectivity are available, there are no extensive experimental validations of these techniques based on single motor unit (MU) activity detection. The aim of this study was to compare spatial selectivity of one- and two-dimensional (1-D and 2-D) spatial filters for EMG signal detection. Intramuscular and surface EMG signals were recorded from the tibialis anterior muscle of ten subjects. The simultaneous use of intramuscular wire and surface recordings (with the spike triggered averaging technique) allowed investigation of the activity of single MUs at the skin surface. The surface EMG signals were recorded with a grid of point electrodes (3 x 3 electrodes) and a ring electrode system at 15 locations over the muscle, with the wires detecting signals from the same intramuscular location. For most subjects, it was possible to classify, from the intramuscular recordings, the activity of the same MUs for all the contractions. The surface EMG signals were averaged with the intramuscularly detected MU action potentials as triggers. In this way, eight spatial filters--longitudinal and transversal, single and double differential (LSD, TSD, LDD, TDD), Laplacian (NDD), inverse binomial filter of the second order (IB2), inverse rectangle filter (IR), and differential ring system (C1)--could be compared on the basis of their spatial selectivity. The distance from the source (transversal with respect to the muscle fiber orientation) after which the surface detected potential did not exceed +/- 5% of the maximal peak-to-peak amplitude (detection distance) was statistically smaller for the 2-D systems and TDD than for the other filters. The MU action potential duration was significantly shorter with LDD and with the 2-D systems than with the other filters. The 2-D filters investigated (including C1) showed very similar performance and were, thus, considered equivalent from the point of view of spatial selectivity.     

Electrode characterization for functional application to upperextremity FNS

A quantitative method has been developed to characterize the isometric force vectors of electrically stimulated paralyzed muscles of the thumb. The vectorial force output as a function of the stimulus level was measured for individual electrode/muscle combinations in a number of intramuscular and epimysial electrodes implanted in paralyzed thenar muscles of cervical level spinal cord injury subejcts. Vectors are used to determine the output characteristics of each electrode/muscle combination. The characteristics studied include: the strength of the contraction, the stimulus level at which fibers from other muscles are stimulated, the recruitment gain of force, dependency of the output on the skeletal position, and the direction of force produced. These characteristics can then be used to select stimulus parameters to produce coordinated hand motion and force generation by functional neuromuscular stimulation (FNS). The range of muscle force and direction for each electrode/muscle combination showed considerable variation between subjects and between electrodes in the same subject. This variation is primarily due to differences in electrode placement within the muscle. Comparison between intramuscular and epimysial electrodes demonstrated similar characteristics in the force vector output. Preliminary results show the potential for using the force vector output to predict the cocontracted output of two muscles.     

Application of a neuro-fuzzy network for gait event detection using electromyography in the child with cerebral palsy

An adaptive neuro-fuzzy inference system (ANFIS) with a supervisory control system (SCS) was used to predict the occurrence of gait events using the electromyographic (EMG) activity of lower extremity muscles in the child with cerebral palsy (CP). This is anticipated to form the basis of a control algorithm for the application of electrical stimulation (ES) to leg or ankle muscles in an attempt to improve walking ability. Either surface or percutaneous intramuscular electrodes were used to record the muscle activity from the quadriceps muscles, with concurrent recording of the gait cycle performed using a VICON motion analysis system for validation of the ANFIS with SCS. Using one EMG signal and its derivative from each leg as its inputs, the ANFIS with SCS was able to predict all gait events in seven out of the eight children, with an average absolute time differential between the VICON recording and the ANFIS prediction of less than 30 ms. Overall accuracy in predicting gait events ranged from 98.6% to 95.3% (root mean-squared error between 0.7 and 1.5). Application of the ANFIS with the SCS to the prediction of gait events using EMG data collected two months after the initial data demonstrated comparable results, with no significant differences between gait event detection times. The accuracy rate and robustness of the ANFIS with SCS with two EMG signals suggests its applicability to ES control.     

Motor Unit Firing Rate During Static Contraction Indicated by the Surface EMG Power Spectrum

In this study, power spectral density functions (PSDF's) were computed of interference EMG of various facial and jaw-elevator muscles during nonfatiguing submaximal static contractions, recorded with surface electrodes. A distinct peak was found in the PSDF's in the frequency region below 40 Hz. It was shown that the peak was due to genuine EMG activity and that it could not be considered as an artifact, which was caused by electrode displacements during contraction. An increase of contraction strength resulted in a shift of the peak to higher frequencies and a decrease of peak amplitude relative to the power spectral estimates above 40 Hz, which were shown to be determined by the shape of the motor unit (MU) action potentials. In accordance with mathematical models of the EMG PSDF, it was demonstrated that the peak indicates the dominant firing rate of the sampled MU's. Our results suggest that this can be defined as the firing rate of the first recruited low-threshold MU's, which may be expected to dominate the interference EMG signal because of their preponderance in number. The data further suggest that the peak can be more readily observed in PSDF's of facial and jaw-elevator muscles than in PSDF's of limb muscles. This might be related to differences in MU firing statistics.     

Using evoked EMG as a synthetic force sensor of isometricelectrically stimulated muscle

A method for the estimation of the force generated by electrically stimulated muscle during isometric contraction is developed here. It is based upon measurements of the evoked electromyogram (EMG) [EEMG] signal. Muscle stimulation is provided to the quadriceps muscle of a paralyzed human subject using percutaneous intramuscular electrodes, and EEMG signals are collected using surface electrodes. Through the use of novel signal acquisition and processing techniques, as well, as a mathematical model that reflects both the excitation and activation phenomena involved in isometric muscle force generation, accurate prediction of stimulated muscle forces is obtained for large time horizons. This approach yields synthetic muscle force estimates for both unfatigued and fatigued states of the stimulated muscle. In addition, a method is developed that accomplishes automatic recalibration of the model to account for day-to-day changes in pickup electrode mounting as well as other factors contributing to EEMG gain variations. It is demonstrated that the use of the measured EEMG as the input to a predictive model of muscle torque generation is superior to the use of the electrical stimulation signal as the model input. This is because the measured EEMG signal captures all of the neural excitation, whereas stimulation-to-torque models only reflect that portion of the neural excitation that results directly from stimulation. The time-varying properties of the excitation process cannot be captured by existing stimulation-to-torque models, but they are tracked by the EEMG-to-torque models that are developed here. This work represents a promising approach to the real-time estimation of stimulated muscle force in functional neuromuscular stimulation applications     

Spatial Filtering of Noninvasive Multielectrode EMG: Part I?Introduction to Measuring Technique and Applications

Complementary to its conventional applications, surface EMG is also suited to gain more detailed information on the functional state of a muscle, when measurement configurations with smaller pickup areas are used. A new category of suitable measurement configurations is obtained by application of the spatial filtering principle to electromyography. In a spatial filter unit, the signals of several recording electrodes are combined to form one output signal channel. The filter characteristic is determined by the weighting factors used and by the geometrical arrangement of the electrodes. Extended multielectrode arrays and multichannel recording make possible the detection of correlated excitations at different sites of the muscle. Even in high levels of muscle contraction, single motor unit impulses that are suitably shaped by filtering can be repeatedly recognized in the surface EMG signal. In clinical studies, pathologically shaped impulses have been identified indicating multiple innervation zones. The initiation and the propagation of excitation within single motor units can be detected with improved accuracy even from very small muscles.     

Inkjet‐Printed Flexible Gold Electrode Arrays for Bioelectronic Interfaces

Bioelectronic interfaces require electrodes that are mechanically flexible and chemically inert. Flexibility allows pristine electrode contact to skin and tissue, and chemical inertness prevents electrodes from reacting with biological fluids and living tissues. Therefore, flexible gold electrodes are ideal for bioimpedance and biopotential measurements such as bioimpedance tomography, electrocardiography (ECG), electroencephalography (EEG), and electromyography (EMG). However, a manufacturing process to fabricate gold electrode arrays on plastic substrates is still elusive. In this work, a fabrication and low‐temperature sintering (≈200 °C) technique is demonstrated to fabricate gold electrodes. At low‐temperature sintering conditions, lines of different widths demonstrate different sintering speeds. Therefore, the sintering condition is targeted toward the widest feature in the design layout. Manufactured electrodes show minimum feature size of 62 μm and conductivity values of 5 × 10 ⁶ S m^?1. Utilizing the versatility of printing and plastic electronic processes, electrode arrays consisting of 31 electrodes with electrode‐to‐electrode spacing ranging from 2 to 7 mm are fabricated and used for impedance mapping of conformal surfaces at 15 kHz. Overall, the fabrication process of an inkjet‐printed gold electrode array that is electrically reproducible, mechanically robust, and promising for bioimpedance and biopotential measurements is demonstrated.     

A multiple-layer finite-element model of the surface EMG signal

The effect of skin, muscle, fat, and bone tissue on simulated surface electromyographic (EMG) signals was examined using a finite-element model. The amplitude and frequency content of the surface potential were observed to increase when the outer layer of a homogeneous muscle model was replaced with highly resistive skin or fat tissue. The rate at which the surface potential decreased as the fiber was moved deeper within the muscle also increased. Similarly, the rate at which the surface potential decayed around the surface of the model, for a constant fiber depth, increased. When layers of subcutaneous fat of increasing thickness were then added to the model, EMG amplitude, frequency content, and the rate of decay of the surface EMG signal around the limb decreased, due to the increased distance between the electrodes and the active fiber. The influence of bone on the surface potential was observed to vary considerably, depending on its location. When located close to the surface of the volume conductor, the surface EMG signal between the bone and the source and directly over the bone increased, accompanied by a slight decrease on the side of the bone distal to the active fiber. The results emphasize the importance of distinguishing between the effects of material properties and the distance between source and electrode when considering the influence of subcutaneous tissue, and suggest possible distortions in the surface EMG signal in regions where a bone is located close to the skin surface.     

Application of higher order statistics techniques to EMG signals to characterize the motor unit action potential

The electromyographic (EMG) signal provides information about the performance of muscles and nerves. At any instant, the shape of the muscle signal, motor unit action potential (MUAP), is constant unless there is movement of the position of the electrode or biochemical changes in the muscle due to changes in contraction level. The rate of neuron pulses, whose exact times of occurrence are random in nature, is related to the time duration and force of a muscle contraction. The EMG signal can be modeled as the output signal of a filtered impulse process where the neuron firing pulses are assumed to be the input of a system whose transfer function is the motor unit action potential. Representing the neuron pulses as a point process with random times of occurrence, the higher order statistics based system reconstruction algorithm can be applied to the EMG signal to characterize the motor unit action potential. In this paper, we report results from applying a cepstrum of bispectrum based system reconstruction algorithm to real wired-EMG (wEMG) and surface-EMG (sEMG) signals to estimate the appearance of MUAPs in the Rectus Femoris and Vastus Lateralis muscles while the muscles are at rest and in six other contraction positions. It is observed that the appearance of MUAPs estimated from any EMG (wEMG or sEMG) signal clearly shows evidence of motor unit recruitment and crosstalk, if any, due to activity in neighboring muscles. It is also found that the shape of MUAPs remains the same on loading.     

Inkjet printed flexible electrodes for surface electromyography

Recording of surface ElctroMyoGraphic (sEMG) signal represents a challenge, due to the nature both stochastic and deterministic of the biopotential. The sEMG signal results from the superimposed activity of a high number of motor units, artifacts, and background noise. Among the different non-invasive techniques to measure the muscle electrical activity, recent studies showed how High-Density surface EMG (HD-sEMG) constitutes a very effective tool. HD-sEMG uses multiple closely spaced electrodes overlying a restricted area of the skin and provides high definition temporal and spatial information on muscle activity. To optimize the features of the current devices for HD-sEMG signal detection, this paper reports on the realization of the first inkjet printed HD electrode matrix. The matrix was built by inkjet printing of a commercial silver-based ink on a flexible Kapton® substrate. While bringing about all advantages of drop-on-demand inkjet technology, such as rapid prototyping and simple CAD re-desing of customizable devices, the printed matrix produces interesting electrical results in terms of resolution, resistance and electrode–skin contact impedance. Electrode–skin impedance obtained with our measurement setup was indeed always in line, while not better than the one obtained with commercial electrodes.     

Design and control of an IPMC artificial muscle finger for micro gripper using EMG signal

This paper presents the design and control of an ionic polymer metal composite (IPMC) based artificial finger for micro gripper. In this work, IPMC based micro finger is actuated by controlled electromyography (EMG) signal. The EMG signal is taken from human index finger via EMG sensor. This signal is pre-amplified before transferring to IPMC for achieving the large bending behavior of IPMC. The bio-mimetic actuation behavior of IPMC is studied by movement of index finger muscles through long tendons. The stability analysis of EMG signal from human index finger is carried out by providing the PID control system. Experimentally, it is observed that IPMC finger can hold the load up to 100 mg when IPMC finger is activated through EMG via human muscles and an IPMC based micro gripper is demonstrated.     

Modeling of surface myoelectric signals. II. Model-based signalinterpretation

Experimental electromyogram (EMG) data from the human biceps brachii were simulated using the model described in [10] of this work. A multichannel linear electrode array, spanning the length of the biceps, was used to detect monopolar and bipolar signals, from which double differential signals were computed, during either voluntary or electrically elicited isometric contractions. For relatively low-level voluntary contractions (10%-30% of maximum force) individual firings of three to four-different motor units were identified and their waveforms were closely approximated by the model. Motor unit parameters such as depth, size, fiber orientation and length, location of innervation and tendonous zones, propagation velocity, and source width were estimated using the model. Two applications of the model are described. The first analyzes the effects of electrode rotation with respect to the muscle fiber direction and shows the possibility of conduction velocity (CV) over- and under-estimation. The second focuses on the myoelectric manifestations of fatigue during a sustained electrically elicited contraction and the interrelationship between muscle fiber CV, spectral and amplitude variables, and the length of the depolarization zone. It is concluded that a) surface EMG detection using an electrode array, when combined with a model of signal propagation, provides a useful method for understanding the physiological and anatomical determinants of EMG waveform characteristics and b) the model provides a way for the interpretation of fatigue plots.     

High-density myoelectric pattern recognition toward improved stroke rehabilitation

Myoelectric pattern-recognition techniques have been developed to infer user's intention of performing different functional movements. Thus electromyogram (EMG) can be used as control signals of assisted devices for people with disabilities. Pattern-recognition-based myoelectric control systems have rarely been designed for stroke survivors. Aiming at developing such a system for improved stroke rehabilitation, this study assessed detection of the affected limb's movement intention using high-density surface EMG recording and pattern-recognition techniques. Surface EMG signals comprised of 89 channels were recorded from 12 hemiparetic stroke subjects while they tried to perform 20 different arm, hand, and finger/thumb movements involving the affected limb. A series of pattern-recognition algorithms were implemented to identify the intended tasks of each stroke subject. High classification accuracies (96.1% ± 4.3%) were achieved, indicating that substantial motor control information can be extracted from paretic muscles of stroke survivors. Such information may potentially facilitate improved stroke rehabilitation.     

The effect of anisotropy on the potential distribution in biological tissue and its impact on nerve excitation simulations

We present a finite difference solution of the potential distribution associated with electrical current stimulation in an anisotropic in-homogeneous tissue environment and compare it to the isotropic case. The results demonstrate that there can be significant errors associated with the assumption of isotropic tissue properties in calculating the potential distribution along an axon in nerve excitation simulations. These errors can have a significant impact on predicted nerve fiber recruitment patterns when evaluating the efficacy of specific surface or intramuscular stimulus electrode configurations. The results of this study also suggest when a more comprehensive tissue model should be implemented in an electrode design study. Simulation results indicate that the isotropy assumption is worst under bipolar electrode stimulation as opposed to monopolar stimulation and that the bipolar error increases as the distance between electrodes decreases. In light of these results, it is concluded that in order to avoid large errors in the calculated potential distribution along an axon, the isotropy assumption should only be used when the transverse depth from the electrode to the nerve is relatively small.     

Myoelectric signal classification for phoneme-based speech recognition

Traditional acoustic speech recognition accuracies have been shown to deteriorate in highly noisy environments. A secondary information source is exploited using surface myoelectric signals (MES) collected from facial articulatory muscles during speech. Words are classified at the phoneme level using a hidden Markov model (HMM) classifier. Acoustic and MES data was collected while the words "zero" through "nine" were spoken. An acoustic expert classified the 18 formative phonemes in low noise levels [signal-to-noise ratio (SNR) of 17.5 dB] with an accuracy of 99%, but deteriorated to approximately 38% under simulations with SNR approaching 0 dB. A fused acoustic-myoelectric multiexpert system, without knowledge of SNR, improved on acoustic classification results at all noise levels. A multiexpert system, incorporating SNR information, obtained accuracies of 99% at low noise levels while maintaining accuracies above 94% during low SNR (0 dB) simulations. Results improve on previous full word MES speech recognition accuracies by almost 10%.     

Modeling of surface myoelectric signals. I. Model implementation

The relationships between the parameters of active motor units (MU's) and the features of surface electromyography (EMG) signals have been investigated using a mathematical model that represents the surface EMG as a summation of contributions from the single muscle fibers. Each MU has parallel fibers uniformly scattered within a cylindrical volume of specified radius embedded in an anisotropic medium. Two action potentials, each modeled as a current tripole, are generated at the neuromuscular junction, propagate in opposite directions and extinguish at the fiber-tendon endings. The neuromuscular junctions and fiber-tendon endings are uniformly scattered within regions of specified width. Muscle fiber conduction velocity and average fiber length to the right and left of the center of the innervation zone are also specified. The signal produced by MU's with different geometries and conduction velocities are superimposed. Monopolar, single differential and double differential signals are computed from electrodes placed in equally spaced locations on the surface of the muscle and are displayed as functions of any of the model's parameters. Spectral and amplitude variables and conduction velocity are estimated from the surface signals and displayed as functions of any of the model's parameters. The influence of fiber-end effects, electrode misalignment, tissue anisotropy, MU's location and geometry are discussed. Part II of this paper will focus on the simulation and interpretation of experimental signals.     

Stretchable human-machine interface based on skin-conformal sEMG electrodes with self-similar geometry

Current stretchable surface electrodes have attracted increasing attention owing to their potential applications in biological signal monitoring,wearable human-machine interfaces (HMIs) and the Internet of Things.The paper proposed a stretchable HMI based on a surface electromyography (sEMG) electrode with a self-similar serpentine configuration.The sEMG electrode was transfer-printed onto the skin surface conformally to monitor biological signals,followed by signal classification and controlling of a mobile robot.Such electrodes can bear rather large deformation (such as ＞30％) under an appropriate areal coverage.The sEMG electrodes have been used to record electrophysiological signals from different parts of the body with sharp curvature,such as the index finger,back of the neck and face,and they exhibit great potential for HMI in the fields of robotics and healthcare.The electrodes placed onto the two wrists would generate two different signals with the fist clenched and loosened.It is classified to four kinds of signals with a combination of the gestures from the two wrists,that is,four control modes.Experiments demonstrated that the electrodes were successfully used as an HMI to control the motion of a mobile robot remotely.     

Simulation of surface EMG signals generated by muscle tissues with inhomogeneity due to fiber pinnation

Surface electromyographic (EMG) signal modeling has important applications in the interpretation of experimental EMG data. Most models of surface EMG generation considered volume conductors homogeneous in the direction of propagation of the action potentials. However, this may not be the case in practice due to local tissue inhomogeneities or to the fact that there may be groups of muscle fibers with different orientations. This study addresses the issue of analytically describing surface EMG signals generated by bi-pinnate muscles, i.e., muscles which have two groups of fibers with two orientations. The approach will also be adapted to the case of a muscle with fibers inclined in the depth direction. Such muscle anatomies are inhomogeneous in the direction of propagation of the action potentials with the consequence that the system can not be described as space invariant in the direction of source propagation. In these conditions, the potentials detected at the skin surface do not travel without shape changes. This determines numerical issues in the implementation of the model which are addressed in this work. The study provides the solution of the nonhomogenous, anisotropic problem, proposes an implementation of the results in complete surface EMG generation models (including finite-length fibers), and shows representative results of the application of the models proposed.