Estimation of motor unit conduction velocity from surface EMG recordings by signal-based selection of the spatial filters

Muscle fiber conduction velocity (CV) can be estimated by the application of a pair of spatial filters to surface electromagnetic (EMG) signals and compensation of the spatial filter transfer function with equivalent temporal filters. This method integrates the selection of the spatial filters for signal detection to the estimation of CV. Using this approach, in this paper, we propose a novel technique for signal-based selection of the spatial filter pair that minimizes the effect of nonpropagating signal components (end-of-fiber effects) on CV estimates (optimal filters). The technique is applicable to signals with one propagating and one nonpropagating component, such as single motor unit action potentials. It is shown that the determination of the optimal filters also allows the identification of the propagating and nonpropagating signal components. The new method was applied to simulated and experimental EMG signals. Simulated signals were generated by a cylindrical, layered volume conductor model. Experimental signals were recorded from the abductor pollicis brevis with a linear array of 16 electrodes. In the simulations, the proposed approach provided CV estimates with lower bias due to nonpropagating signal components than previously proposed methods based on the entire signal waveform. In the experimental signals, the technique separated propagating and nonpropagating signal components with an average reconstruction error of 2.9 +/- 0.9% of the signal energy. The technique may find application in single motor unit studies for decreasing the variability and bias of CV estimates due to the presence and different weights of the nonpropagating components.     

Assessment of average muscle fiber conduction velocity from surface EMG signals during fatiguing dynamic contractions

In this paper, we propose techniques of surface electromyographic (EMG) signal detection and processing for the assessment of muscle fiber conduction velocity (CV) during dynamic contractions involving fast movements. The main objectives of the study are: 1) to present multielectrode EMG detection systems specifically designed for dynamic conditions (in particular, for CV estimation); 2) to propose a novel multichannel CV estimation method for application to short EMG signal bursts; and 3) to validate on experimental signals different choices of the processing parameters. Linear adhesive arrays of electrodes are presented for multichannel surface EMG detection during movement. A new multichannel CV estimation algorithm is proposed. The algorithm provides maximum likelihood estimation of CV from a set of surface EMG signals with a window limiting the time interval in which the mean square error (mse) between aligned signals is minimized. The minimization of the windowed mse function is performed in the frequency domain, without limitation in time resolution and with an iterative computationally efficient procedure. The method proposed is applied to signals detected from the vastus laterialis and vastus medialis muscles during cycling at 60 cycles/min. Ten subjects were investigated during a 4-min cycling task. The method provided reliable assessment of muscle fatigue for these subjects during dynamic contractions.     

Short time Fourier analysis of the electromyogram: fast movements and constant contraction

We applied short-time Fourier analysis to surface electromyograms (EMG) recorded during rapid movements, and during isometric contractions at constant forces. We selected a portion of the data to be transformed by multiplying the signal by a Hamming window, then computed the discrete Fourier transform. Shifting the window along the data record, we computed a new spectrum each 10 ms. We displayed the transformed data in spectograms or "voiceprints." This short-time technique allowed us to see time-dependencies in the EMG that are normally averaged in the Fourier analysis of these signals. Spectra of EMG's during isometric contractions at constant force vary in the short (10-20 ms) term. Moments of the spectral distribution show this variability. Short-time spectra from EMG's recorded during rapid movements were much less variable. The windowing technique picked out the typical "three-burst pattern" in EMG's from both wrist and head movements. Spectra during the bursts were more consistent than those during isometric contractions. Furthermore, there was a consistent shift in spectral statistics in the course of the three bursts. Both the center frequency and the variance of the spectral energy distribution grew from the first burst to the second burst in the same muscle. We discuss this pattern with respect to the origin of the EMG bursts in rapid movement. We also extend the analogy between electromyograms and speech signals to argue for future applicability of short-time spectral analysis of EMG.     

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.     

A novel approach for estimating muscle fiber conduction velocity by spatial and temporal filtering of surface EMG signals

We describe a new method for the estimation of muscle fiber conduction velocity (CV) from surface electromyography (EMG) signals. The method is based on the detection of two surface EMG signals with different spatial filters and on the compensation of the spatial filtering operations by two temporal filters (with CV as unknown parameter) applied to the signals. The transfer functions of the two spatial filters may have different magnitudes and phases, thus the detected signals have not necessarily the same shape. The two signals are first spatially and then temporally filtered and are ideally equal when the CV value selected as a parameter in the temporal filters corresponds to the velocity of propagation of the detected action potentials. This approach is the generalization of the classic spectral matching technique. A theoretical derivation of the method is provided together with its fast implementation by an iterative method based on the Newton's method. Moreover, the lowest CV estimate among those obtained by a number of filter pairs is selected to reduce the CV bias due to nonpropagating signal components. Simulation results indicate that the method described is less sensitive than the classic spectral matching approach to the presence of nonpropagating signals and that the two methods have similar standard deviation of estimation in the presence of additive, white, Gaussian noise. Finally, experimental signals have been collected from the biceps brachii muscle of ten healthy male subjects with an adhesive linear array of eight electrodes. The CV estimates depended on the electrode location with positive bias for the estimates from electrodes close to the innervation or tendon regions, as expected. The proposed method led to significantly lower bias than the spectral matching method in the experimental conditions, confirming the simulation results.     

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.     

Entropy-based optimization of wavelet spatial filters

A new class of spatial filters for surface electromyographic (EMG) signal detection is proposed. These filters are based on the 2-D spatial wavelet decomposition of the surface EMG recorded with a grid of electrodes and inverse transformation after zeroing a subset of the transformation coefficients. The filter transfer function depends on the selected mother wavelet in the two spatial directions. Wavelet parameterization is proposed with the aim of signal-based optimization of the transfer function of the spatial filter. The optimization criterion was the minimization of the entropy of the time samples of the output signal. The optimized spatial filter is linear and space invariant. In simulated and experimental recordings, the optimized wavelet filter showed increased selectivity with respect to previously proposed filters. For example, in simulation, the ratio between the peak-to-peak amplitude of action potentials generated by motor units 20 degrees apart in the transversal direction was 8.58% (with monopolar recording), 2.47% (double differential), 2.59% (normal double differential), and 0.47% (optimized wavelet filter). In experimental recordings, the duration of the detected action potentials decreased from (mean +/- SD) 6.9 +/- 0.3 ms (monopolar recording), to 4.5 +/- 0.2 ms (normal double differential), 3.7 +/- 0.2 (double differential), and 3.0 +/- 0.1 ms (optimized wavelet filter). In conclusion, the new class of spatial filters with the proposed signal-based optimization of the transfer function allows better discrimination of individual motor unit activities in surface EMG recordings than it was previously possible.     

A comparison of the effects of electrode implantation and targeting on pattern classification accuracy for prosthesis control

The use of surface versus intramuscular electrodes as well as the effect of electrode targeting on pattern-recognition-based multifunctional prosthesis control was explored. Surface electrodes are touted for their ability to record activity from relatively large portions of muscle tissue. Intramuscular electromyograms (EMGs) can provide focal recordings from deep muscles of the forearm and independent signals relatively free of crosstalk. However, little work has been done to compare the two. Additionally, while previous investigations have either targeted electrodes to specific muscles or used untargeted (symmetric) electrode arrays, no work has compared these approaches to determine if one is superior. The classification accuracies of pattern-recognition-based classifiers utilizing surface and intramuscular as well as targeted and untargeted electrodes were compared across 11 subjects. A repeated-measures analysis of variance revealed that when only EMG amplitude information was used from all available EMG channels, the targeted surface, targeted intramuscular, and untargeted surface electrodes produced similar classification accuracies while the untargeted intramuscular electrodes produced significantly lower accuracies. However, no statistical differences were observed between any of the electrode conditions when additional features were extracted from the EMG signal. It was concluded that the choice of electrode should be driven by clinical factors, such as signal robustness/stability, cost, etc., instead of by classification accuracy.     

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.     

Blind separation of linear instantaneous mixtures of nonstationary surface myoelectric signals

Electromyographic (EMG) recordings detected over the skin may be mixtures of signals generated by different active muscles due to the phenomena related to volume conduction. Separation of the sources is necessary when single muscle activity has to be detected. Signals generated by different muscles may be considered uncorrelated but in general overlap in time and frequency. Under certain assumptions, mixtures of surface EMG signals can be considered as linear instantaneous but no a priori information about the mixing matrix is available when different muscles are active. In this study, we applied blind source separation (BSS) methods to separate the signals generated by two active muscles during a force-varying task. As the signals are non stationary, an algorithm based on spatial time-frequency distributions was applied on simulated and experimental EMG signals. The experimental signals were collected from the flexor carpi radialis and the pronator teres muscles which could be activated selectively for wrist flexion and rotation, respectively. From the simulations, correlation coefficients between the reference and reconstructed sources were higher than 0.85 for signals largely overlapping both in time and frequency and for signal-to-noise ratios as low as 5 dB. The Choi-Williams and Bessel kernels, in this case, performed better than the Wigner-Ville one. Moreover, the selection of time-frequency points for the procedure of joint diagonalization used in the BSS algorithm significantly influenced the results. For the experimental signals, the interference of the other source in each reconstructed source was significantly attenuated by the application of the BSS method. The ratio between root-mean-square values of the signals from the two sources detected over one of the muscles increased from (mean +/- standard deviation) 2.33 +/- 1.04 to 4.51 +/- 1.37 and from 1.55 +/- 0.46 to 2.72 +/- 0.65 for wrist flexion and rotation, respectively. This increment was statistically significant. It was concluded that the BSS approach applied is promising for the separation of surface EMG signals, with applications ranging from muscle assessment to detection of muscle activation intervals, and to the control of myoelectric prostheses.     

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.     

Low delay coder (< 25 ms) of wideband audio (20 Hz-15 kHz) scalable from 64 to 32 kbit/s

A low delay coder for speech and music signals sampled at 32kHz is described. Its algorithmic delay does not exceed 25 ms which enables audioconferencing applications without echo cancellation. Its bit rate is scalable between 64 and 32 kbit/s by steps of 8 kbit/s. The transmitter issues the binary code at 64 kbit/s with lower bit rate codes embedded in it. The receiver may operate at lower bit rates with gradual loss of quality. The proposed coder is based on a mixed scheme : the adopted solution contains elements from the CELP speech coder and frequency domain music coders. The perceptual signal is obtained in the time domain, then transformed to the frequency domain where bit allocation is calculated and transform coefficients are quantized. A first solution based on the dft is discussed, then a second solution based on a mdct with small overlap is applied. The quantization of these coefficients is done in the following way. First, a prediction of the whole spectrum is applied. Then, a mean- removed gain- shape split vq is used for amplitude spectrum quantization and a hierarchical 2- dimensional vq is used for phase spectrum quantization with amplitude correction. At the phase quantization stage, each codeword describing the selected vector index is split into parts corresponding to different bit rates. Due to the hierarchical codebook structure, truncated indices may be used, without much affecting the signal quality. Simulation results are presented and the robustness of the proposed coder is examined.     

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.     

A Reliable Myoelectric Signal Detector Based on the Propagation Characteristics of Motor Unit Action Potentials

A method was proposed for detecting and rejecting motion artifacts superimposed on myoelectric (ME) signals which are used in the estimation of muscular activity, in the control of powered prostheses, and in other applications. The method is based on the propagation characteristics of motor unit action potentials derived with multiple surface electrodes placed along the muscle fibers. The contamination of artifacts was detected by the decrease of the normalized correlation coefficient calculated at the time shift corresponding to the potential propagation. The product of two correlated signals was found to be less affected by the artifacts and was a better estimate of muscular activity than the root mean square of the ME signal which is conventionally used in the applications of ME signals.     

High-precision EMG signal decomposition using communicationtechniques

This paper presents a new approach to the decomposition of electromyographic (EMG) signals. EMG signals consist of a superposition of delayed finite-duration waveforms that carry the information about the firing of different muscle fiber groups. The new approach is based on a communication technical interpretation of the EMG signal. The source is modeled as a signaling system with intersymbol-interference, which encodes a well defined sparse information sequence. This point of view allows a maximum-likelihood (ML) as well as a maximum a posteriori (MAP) estimation of the underlying firing pattern to be made. The high accuracy attainable with the proposed method is illustrated both with measured and artificially generated EMG signals     

基于灰度理论模型的截肢受试者手势分类方法研究

针对截肢者手势动作特征提取复杂、动作识别率较低的问题,该文提出一种基于灰度模型的特征提取方法。首先对预处理后的肌电信号与加速度信号经滑动窗信号截取。然后提取表面肌电信号均值、灰度模型的驱动项系数和加速度信号的绝对值均值构成特征向量,最后对滑动窗截取信号特征进行连续的识别。该文采用NinaPro(Non invasive adaptive Prosthetics)公开数据集对提出的方法进行验证,实验表明该文算法能够有效提取肌电和加速度信号的特征,对9名截肢受试者的17类手势动作的平均识别率达到91.14%,提高了17类手势的识别准确率,为仿生假肢人机交互控制算法提供了一种新的思路。     

Fatigue analysis of the surface EMG signal in isometric constant force contractions using the averaged instantaneous frequency

The averaged instantaneous frequency (AIF) is proposed as an alternative method for the frequency analysis of surface electromyography (EMG) in the study of muscle fatigue during sustained, isometric muscle contractions. Results from performance analysis using experimental EMG signals demonstrate the low variability of the proposed frequency variable. Indeed, the AIF measure is shown to perform significantly better than the widely used mean and median frequency variables, in terms of robustness to the length of the analysis window.     

Extracting Simultaneous and Proportional Neural Control Information for Multiple-DOF Prostheses From the Surface Electromyographic Signal

A novel signal processing algorithm for the surface electromyogram (EMG) is proposed to extract simultaneous and proportional control information for multiple DOFs. The algorithm is based on a generative model for the surface EMG. The model assumes that synergistic muscles share spinal neural drives, which correspond to the intended activations of different DOFs of natural movements and are embedded within the surface EMG. A DOF-wise nonnegative matrix factorization (NMF) is developed to estimate neural control information from the multichannel surface EMG. It is shown, both by simulation and experimental studies, that the proposed algorithm is able to extract the multidimensional control information simultaneously. A direct application of the proposed method would be providing simultaneous and proportional control of multifunction myoelectric prostheses.      

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.     

EMG-based speech recognition using hidden markov models with global control variables

It is well known that a strong relationship exists between human voices and the movement of articulatory facial muscles. In this paper, we utilize this knowledge to implement an automatic speech recognition scheme which uses solely surface electromyogram (EMG) signals. The sequence of EMG signals for each word is modelled by a hidden Markov model (HMM) framework. The main objective of the work involves building a model for state observation density when multichannel observation sequences are given. The proposed model reflects the dependencies between each of the EMG signals, which are described by introducing a global control variable. We also develop an efficient model training method, based on a maximum likelihood criterion. In a preliminary study, 60 isolated words were used as recognition variables. EMG signals were acquired from three articulatory facial muscles. The findings indicate that such a system may have the capacity to recognize speech signals with an accuracy of up to 87.07%, which is superior to the independent probabilistic model.