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.

Electrophysiological effects of aerobic fitness and maximal aerobic exercise were investigated by comparing P300 and N400 before and after a maximal cycling test. Event-related potentials (ERPs) were obtained from 20 students divided into two matched groups defined by their aerobic fitness level (cyclists vs. sedentary subjects). The session of postexercise ERPs was performed immediately after body temperature and heart rate returned to preexercise values. At rest, no significant differences were observed in ERP parameters between cyclists and sedentary subjects. This finding argued against the hypothesis that ERP modifications may be directly assumed by aerobic fitness level. The postexercise session of ERPs showed a significant P300 amplitude increase and a significant P300 latency decrease in all subjects. Similarly, N400 effect increased significantly after the maximal exercise in all subjects. ERP changes were of the same magnitude in the two groups. The present study argues for a general arousing effect of maximal aerobic exercise, independently of the aerobic fitness level.

A pseudojoint estimation of time scale and time delay between an unknown deterministic transient type signal and a reference signal is proposed. The method is based on the separation between the estimations of the two dependent parameters. The time autocorrelation function (TACF) preserves the time scale and is invariant with respect to the time delay between the signals. The time scale factor can, thus, be estimated independently from time delay using the TACFs of the two signals. After estimating the time scale factor, the signal can be scaled by the estimated amount. The time delay is then estimated without bias due to the time scale factor. To obtain high resolution joint estimates, the time scale factor is estimated in the scale domain from the scale transforms of the TACFs of the two signals. The proposed method has low computational cost. Moreover, the results on synthetic signals show good performance of the method with respect to the Cramér-Rao Lower Bound and the joint Maximum Likelihood Estimation. A possible application of the technique to the analysis of electromyogram (EMG) signals detected during electrically elicited contractions is also presented. In a few representative cases, it is shown that the time scale estimate reveals myoelectric manifestations of muscle fatigue and is less affected by M-wave truncation than spectral EMG attributes.

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