R.N. Scott

A new strategy for multifunction myoelectric control

A novel approach to the control of a multifunction prosthesis based on the classification of myoelectric patterns is described. It is shown that the myoelectric signal exhibits a deterministic structure during the initial phase of a muscle contraction. Features are extracted from several time segments of the myoelectric signal to preserve pattern structure. These features are then classified using an artificial neural network. The control signals are derived from natural contraction patterns which can be produced reliably with little subject training. The new control scheme increases the number of functions which can be controlled by a single channel of myoelectric signal but does so in a way which does not increase the effort required by the amputee. Results are presented to support this approach.<<ETX>>

The application of neural networks to myoelectric signal analysis: a preliminary study

Two neural network implementations are applied to myoelectric signal (MES) analysis tasks. The motivation behind this research is to explore more reliable methods of deriving control for multi-degree-of-freedom arm prostheses. A discrete Hopfield network is used to calculate the time series parameter for a moving average MES model. It is demonstrated that the Hopfield network is capable of generating the same time series parameters as those produced by the conventional sequential least-squares algorithm. Furthermore, it can be extended to applications utilizing larger amounts of data, and possibly to higher-order time series models, without significant degradation in computational efficiency. The second neural network implementation involves using a two-layer perceptron for classifying a single-site MES on the basis of two features, the first time series parameter and the signal power.<<ETX>>

Computer terminal work and the benefit of microbreaks

Microbreaks are scheduled rest breaks taken to prevent the onset or progression of cumulative trauma disorders in the computerized workstation environment. The authors examined the benefit of microbreaks by investigating myoelectric signal (MES) behavior, perceived discomfort, and worker productivity while individuals performed their usual keying work. Participants were randomly assigned to one of three experimental groups. Each participant provided data from working sessions where they took no breaks, and from working sessions where they took breaks according to their group assignment: microbreaks at their own discretion (control), microbreaks at 20 min intervals, and microbreaks at 40 min intervals. Four main muscle areas were studied: the cervical extensors, the lumbar erector spinae, the upper trapezius/supraspinatus, and the wrist and finger extensors. The authors have previously shown that when computer workers remained seated at their workstation, the muscles performing sustained postural contractions displayed a cyclic trend in the mean frequency (MNF) of the MES (McLean et al., J. Electrophysiol. Kinesiol. 10 (1) (2000) 33). The data provided evidence (p < 0.05) that all microbreak protocols were associated with a higher frequency of MNF cycling at the wrist extensors, at the neck when microbreaks were taken by the control and 40 min protocol groups, and at the back when breaks were taken by the 20 and 40 min protocol groups. No significant change in the frequency of MNF cycling was noted at the shoulder. It was determined (p < 0.05) that microbreaks had a positive effect on reducing discomfort in all areas studied during computer terminal work, particularly when breaks were taken at 20 min intervals. Finally, microbreaks showed no evidence of a detrimental effect on worker productivity. The underlying cause of MNF cycling, and its relationship to the development of discomfort or cumulative trauma disorders remains to be determined.

A Nonstationary Model for the Electromyogram

A theoretical model of the electromyographic (EMG) signal has been developed. In the model, the neural pulse train inputs were considered to be point processes which passed through linear, time-invariant systems that represented the respective motor unit action potential. The outputs were then summed to produce the EMG. It was assumed, that in the production of muscle force, the controlled parameter was the number of active motor units, n(t). The model then showed that the EMG can be represented as an amplitude modulation process of the form EMG = [Kn(t)1/2 w(t) with the stochastic process, w(t), having the spectral and probability characteristics of the EMG during a constant contraction. Various assumptions made in the model development have been verified by experiments.

Signal processing for the multistate myoelectric channel

In the multistate myoelectric channel, a single myoelectric signal source is used to control a multifunction powered prosthesis. The selection of a prosthesis function requires a receiver to process the myoelectric signal, contaminated with noise, and to decide on the basis of the received signals which function is desired. Thus the channnel cleady presents a problem of choice of receiver and of decision strategy. Previous sotutions to this problem have been basically empirical. In this paper we seek the optimum receiver where optimum is in the minimum probability of error sense. First a model is developed for the bipolar myoelectric signal to provide information about the relevant signal parameters and statistics. Using this information the Bayes minimum probability of error receiver is derived for an orbitrary signal parameter set. The optimum signal parameter set is then found for the Bayes receiver, and the receiver performance calculated. The receiver performance is measured and compared with the calculated performance. A significant performance improvement is seen in the optimum receiver over a more conventional receiver.

The short-time Fourier transform and muscle fatigue assessment in dynamic contractions.

The mean frequency of the power spectrum of an electromyographic signal is an accepted index for monitoring fatigue in static contractions. There is however, indication that it may be a useful index even in dynamic contractions in which muscle length and/or force may vary. The objective of this investigation was to explore this possibility. An examination of the effects of amplitude modulation on modeled electromyographic signals revealed that changes in variance created in this way do not sufficiently affect characteristic frequency data to obscure a trend with fatigue. This validated the contention that not all non-stationarities in signals necessarily manifest in power spectral parameters. While an investigation of the nature and effects of non-stationarities in real electromyographic signals produced from dynamic contractions indicated that a more complex model is warranted, the results also indicated that averaging associated with estimating spectral parameters with the short-time Fourier transform can control the effects of the more complex non-stationarities. Finally, a fatigue test involving dynamic contractions at a force level under 30% of peak voluntary dynamic range, validated that it was possible to track fatigue in dynamic contractions using a traditional short-time Fourier transform methodology.

Noise characteristics of stainless-steel surface electrodes

Bioelectric events measured with surface electrodes are subject to noise components which may be significant in comparison with low-level biological signals such as evoked neuroelectric potentials, and myoelectric potentials. In an effort to better understand noise arising from these electrodes, electrode and measurement system noise is modelled. The effect of electrode surface area on electrode impedance and noise is studied using circular stainless-steel electrodes of varying diameters. The main contributions of the work are the development of a model for stainless-steel electrode noise as a function of electrode area, and demonstrating that, for the band-width of interest to evoked neuroelectric and myoelectric signals (8–10 000 Hz), the primary noise components are thermal and amplifier current generated. The magnitudes of both of these depend on the electrode impedance magnitude. Electrode impedance is shown to be a power function of both electrode diameter and frequency, consistent with a capacitive electrode model.

Two-channel enhancement of a multifunction control system

The enhancement of an existing myoelectric control system has been investigated. The original one-channel system used an artificial neural network to classify myoelectric patterns. This research shows that a two-channel control system can improve the classification accuracy of the pattern classifier significantly, thus improving the reliability of the prosthesis.<<ETX>>

Signal Processing for Proportional Myoelectric Control

Proportional myoelectric control of powered prostheses requires the estimation of a time-varying control signal from the patients myoelectric signal. Since the myoelectric signal is a zero-mean stochastic process, a nonlinearity is a necessary element of the estimator. Typically, a full-wave rectifier is used for this nonlinearity, followed by a low-pass filter to complete the estimation of the control signal. In this work, it is proposed to use a logarithmic nonlinearity, followed by a linear minimum mean-square error estimator. The logarithmic nonlinearity maps the myoelectric signal into an additive control signal-plus-noise domain in which the Kalman filter is employed to estimate the control signal. The theoretical performance of this estimator is obtained and verified by experiments.

Adaptive stimulus artifact reduction in noncortical somatosensory evoked potential studies

Somatosensory evoked potentials (SEPs) are an important class of bioelectric signals which contain clinically valuable information. The surface measurements of these potentials are often contaminated by a stimulus evoked artifact. The stimulus artifact (SA), depending upon the stimulator and measurement system characteristics, may obscure some of the information carried by the SEPs. Conventional methods for SA reduction employ hardware-based circuits which attempt to eliminate the SA by blanking the input during SA period. However, there is a danger of losing some of the important SEP information, especially if the stimulating and recording electrodes are close together. Here, the authors apply both linear and nonlinear adaptive filtering techniques to the problem of SA reduction. Nonlinear adaptive filters (NAFs) based on truncated second-order Volterra series expansion are discussed and their applicability to SA cancellation is explored through processing both simulated and in vivo SEP data. The performances of the NAF and the finite impulse response (FIR) linear adaptive filter (LAF) are compared by processing experimental SEP data collected from different recording sites. Due to the inherent nonlinearities in the generation of the SA, the NAF is shown to achieve significantly better SA cancellation compared to the LAF.