This brief review examines some of the methods used to infer central control strategies from surface electromyogram (EMG) recordings. Among the many uses of the surface EMG in studying the neural control of movement, the review critically evaluates only some of the applications. The focus is on the relations between global features of the surface EMG and the underlying physiological processes. Because direct measurements of motor unit activation are not available and many factors can influence the signal, these relations are frequently misinterpreted. These errors are compounded by the counterintuitive effects that some system parameters can have on the EMG signal. The phenomenon of crosstalk is used as an example of these problems. The review describes the limitations of techniques used to infer the level of muscle activation, the type of motor unit recruited, the upper limit of motor unit recruitment, the average discharge rate, and the degree of synchronization between motor units. Although the global surface EMG is a useful measure of muscle activation and assessment, there are limits to the information that can be extracted from this signal.

The extraction of neural strategies from the surface EMG

Pattern recognition

Comparison of algorithms for estimation of EMG variables during voluntary isometric contractions.

The linear electrode array : a useful tool with many applications

During isometric contractions of increasing strength, motor units (MUs) are recruited by the central nervous system in an orderly manner starting with the smallest, with muscle fibers that usually show the lowest conduction velocity (CV). Theory predicts that the higher the velocity of propagation of the action potential, the higher the power at high frequencies of the detected surface signal. These considerations suggest that the power spectral density of the surface detected electromyogram (EMG) signal may give indications about the MU recruitment process. The purpose of this paper is to investigate the potential and limitations of spectral analysis of the surface EMG signal as a technique for the investigation of muscle force control. The study is based on a simulation approach and on an experimental investigation of the properties of surface EMG signals detected from the biceps brachii during isometric linearly increasing torque contractions. Both simulation and experimental data indicate that volume conductor properties play an important role as confounding factors that may mask any relation between EMG spectral variables and estimated CV as a size principle parameter during ramp contractions. The correlation between spectral variables and CV is thus significantly lower when the MU pool is not stable than during constant-torque isometric contractions. Our results do not support the establishment of a general relationship between spectral EMG variables and torque or recruitment strategy.

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Motor unit recruitment strategies investigated by surface EMG variables.

This work addresses the problem of estimating the conduction velocity (CV) of single motor unit (MU) action potentials from surface EMG signals detected with linear electrode arrays during voluntary muscle contractions. In ideal conditions, that is without shape or scale changes of the propagating signals and with additive white Gaussian noise, the maximum likelihood (ML) is the optimum estimator of delay. Nevertheless, other methods with computational advantages can be proposed; among them, a modified version of the beamforming algorithm is presented and compared with the ML estimator. In real cases, the resolution in delay estimation in the time domain is limited because of the sampling process. Transformation to the frequency domain allows a continuous estimation. A fast, high-resolution implementation of the presented multichannel techniques in the frequency domain is proposed. This approach is affected by a negligible decrease in performance with respect to ideal interpolation. Application of the ML estimator, based on two-channel information, to ten firings of each of three MUs provides a CV estimate affected by a standard deviation of 0.5 m s(-1); the modified beamforming and ML estimators based on five channels provide a CV standard deviation of less than 0.1 m s(-1) and allow the detection of statistically significant differences between the CVs of the three MUs. CV can therefore be used for MU classification.

Estimation of single motor unit conduction velocity from surface electromyogram signals detected with linear electrode arrays.

Estimation of single motor unit conduction velocity from myoelectric signals detected with linear electrode arrays

The study of the electroencephalographic (EEG) signal contributes to sleep analysis. In the microstructure of the sleep EEG signal, transient patterns are characterized by their frequency content and their time duration. The Time-Frequency Representations (TFR) take into account these time frequency characteristics but the lower energy transient signals are masked by higher energy ones. In order to overcome this problem, we introduced a method to decompose signals into a summation of oscillatory components with time varying frequency, amplitude and phase characteristics, based on the Tufts-Kumaresan algorithm. The resulting parameters, i.e. amplitude and frequency, are then used to train joint linear filtering operations of the TFR in the time frequency domain. The aim of this work is to improve the classical TFR analysis for detecting frequency transients over short time duration, to reduce the amount of useful information to few parameters that help medical doctors to analyze the microstructure of sleep by correlating information estimated from different signals.

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E. Fortunato

Papers

Estimation of single motor unit conduction velocity from surface electromyogram signals detected with linear electrode arrays.

Estimation of single motor unit conduction velocity from myoelectric signals detected with linear electrode arrays

Combining time frequency representation and parametric analysis for the enhancement of transients in sleep EEG signal