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. Nonlinear surface EMG analysis to detect changes of motor unit conduction velocity and synchronization. J Appl Physiol 93: 1753-1763. First published August 9, 2002 10.1152/japplphysiol.00314.2002Amplitude and frequency content of the surface electromyographic (EMG) signal reflect central and peripheral modifications of the neuromuscular system. Classic surface EMG spectral variables applied to assess muscle functions are the centroid and median power spectral frequencies. More recently, nonlinear tools have been introduced to analyze the surface EMG; among them, the recurrence quantification analysis (RQA) was shown to be particularly promising for the detection of muscle status changes. The purpose of this work was to analyze the effect of motor unit short-term synchronization and conduction velocity (CV) on EMG spectral variables and two variables extracted by RQA, the percentage of recurrence (%Rec) and determinism (%Det). The study was performed on the basis of a simulation model, which allowed changing the degree of synchronization and mean CV of a number of motor units, and of an experimental investigation of the surface EMG signal properties detected during high-force-level isometric fatiguing contractions of the biceps brachii muscle. Simulations and experimental results were largely in agreement and show that 1) spectral variables, %Rec, and %Det are influenced by CV and degree of synchronization; 2) spectral variables are highly correlated with %Det (R ϭ Ϫ0.95 in the simulations and Ϫ0.78 and Ϫ0.75 for the initial values and normalized slopes, respectively, in the experimental signals), and thus the information they provide on muscle properties is basically the same; and 3) variations of %Det and %Rec in response to changes in muscle properties are significantly larger than the variations of spectral variables. This study validates RQA as a means for fatigue assessment with potential advantages (such as the higher sensitivity to changes of muscle status) with respect to the classic spectral analysis. surface electromyography; motor unit short-term synchronization; surface electromyographic modeling; recurrence plot analysis THE SURFACE ELECTROMYOGRAPHIC (EMG) signal presents smaller bandwidth with respect to the intramuscular EMG, because the tissues separating the muscle fibers and the recording electrodes act as low-pass filters (15,28). This determines low-spatial selectivity, which hinders the separation of the contributions of different motor units (MUs). For this reason, past research efforts in the surface EMG field were mainly devoted to the development of processing techniques in time and frequency domain, which gave indications about the global EMG activity, without aiming at an analysis at the single MU level. Although, recently, techniques for noninvasive assessment of single MU properties have been proposed and refined (4,6,8), their applicability is still limited by the need of complex detection systems, to low-contraction levels, and controlled laboratory conditions for signal recordi...

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This paper presents a study of the performance of various local and spherical spline methods currently in use for the surface Laplacian (SL) estimate of scalp potential distributions. The SL was estimated from simulated instantaneous event-related scalp potentials generated over a three-shell spherical head model. Laplacian estimators used planar and spherical scalp models. Noise of increasing magnitude and spatial frequency was added to the potential distributions in order to simulate noise presumed to contaminate scalp-recorded event-related potentials. A comparison of noise effects on various Laplacian estimates was made for increasing number of "electrode" positions in variants of the 10-20 system. Furthermore, to evaluate the error due to the use of unrealistic scalp models, the matching between SL estimates of human scalp-recorded movement-related potentials computed on spherical and realistically-shaped MRI-constructed models of the scalp was examined. With all methods the error of the SL estimate increased proportionally with the magnitude and spatial frequency of noise. Increased number of "electrodes" up to 256 significantly reduced the error (p < 0.05). In general, the best SL estimates were computed by second and third order splines including lambda correction, the performances of the second order spline being better with more than 64 "electrodes". Compared with spline Laplacians, the best local methods provided nearly equal estimates with low spatial sampling (19 and 28 "electrodes"), as well as high spatial frequency noise. The error of the SL estimate due to unrealistic scalp model was significant, and it augmented with increased spatial sampling from 64 to 128 electrodes.

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