Many previous studies were focused on the influence of anatomical, physical, and detection-system parameters on recorded surface EMG signals. Most of them were conducted by simulations. Previous EMG models have been limited by simplifications which did not allow simulation of several aspects of the EMG generation and detection systems. We recently proposed a model for fast and accurate simulation of the surface EMG. It characterizes the volume conductor as a non-homogeneous and anisotropic medium, and allows simulation of EMG signals generated by finite-length fibers without approximation of the current-density source. The influence of thickness of the subcutaneous tissue layers, fiber inclination, fiber depth, electrode size and shape, spatial filter transfer function, interelectrode distance, length of the fibers on surface, single-fiber action-potential amplitude, frequency content, and estimated conduction velocity are investigated in this paper. Implications of the results on electrode positioning procedures, spatial filter design, and EMG signal interpretation are discussed.

Two physiological factors are assumed in this paper to mainly determine the myoelectric manifestations of fatigue: (1) the decrease of the conduction velocity (CV) of motor unit action potentials (MUAP) (peripheral fatigue), and (2) the increase of MU synchronization by the central nervous system (central fatigue). To describe separately the peripheral and central components of the myoelectric manifestations of fatigue, we investigated the following indexes: (1) mean spectral frequency - MNF, (2) median spectral frequency - MDF, (3) root mean square - RMS, (4) average rectified value - ARV, (5) estimation of muscle fiber conduction velocity - ECV, (6) percentage of determinism - %DET, (7) spectral indexes defined as the ratio between signal spectral moments - FI(k), (8) MNF estimated by autoregressive analysis - MNF(AR), (9) MNF estimated by Choi-Williams time-frequency representation - MNF(CWD), (10) MNF estimated by continuous wavelet transform - MNF(CWT), (11) signal entropy - S, (12) fractal dimension - FD. The indexes were tested with a set of synthetic EMG signals, with different CV distribution and level of MU synchronization. The indexes were calculated on epochs of 0.5s. It was observed that ECV is uncorrelated with the level of simulated synchronization (promising index of peripheral fatigue). On the other hand FD was the index least affected by CV changes and most related to the level of synchronism (promising index of central fatigue). A representative application to some experimental signals from vastus lateralis muscle during an isometric endurance test supported the results of the simulations. The vector (ECV, FD) is suggested to provide selective indications of peripheral and central fatigue. The description of EMG fatigue by a bi-dimensional vector opens new perspectives in the assessment of muscle properties, with potential application in both clinical and sport sciences.

To understand better the features of the mechanomyogram (MMG) with different force levels and muscle architectures, the MMG signals detected at many points along three muscles were analysed by the application of a linear array of MMG sensors (up to eight) over the skin. MMG signals were recorded from the biceps brachii, tibialis anterior and upper trapezius muscles of the dominant side of ten healthy male subjects. The accelerometers were aligned along the direction of the muscle fibres. One accelerometer was located over the distal muscle innervation zone, and the other six or seven accelerometers were placed over the muscle, forming an array of sensors with fixed distances between them. The array covered almost the entire muscle length in all cases. MMG signals detected from adjacent accelerometers had similar shapes, with correlation coefficients ranging from about 0.5 to about 0.9. MMG amplitude and characteristic spectral frequencies significantly depended on accelerometer location. The MMG amplitude was maximum at the muscle belly for the biceps brachii and the tibialis anterior. Higher MMG characteristic spectral frequencies were associated with higher amplitudes in the case of the biceps brachii, whereas the opposite was observed for the tibialis anterior muscle. In the upper trapezius, the relationship between characteristic spectral frequencies, MMG amplitude and contraction force depended on the accelerometer location. This suggested that MMG spectral features do not only reflect the mechanical properties of the recruited muscle fibres but depend on muscle architecture and motor unit territorial distribution. It was concluded that the location of the accelerometer can have an influence on both amplitude and spectral MMG features, and this dependence should be considered when MMG signals are used for muscle assessment.

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New recording techniques for detecting surface electromyographic (EMG) signals based on concentric-ring electrodes are proposed in this paper. A theoretical study of the two-dimensional (2-D) spatial transfer function of these recording systems is developed both in case of rings with a physical dimension and in case of line rings. Design criteria for the proposed systems are presented in relation to spatial selectivity. It is shown that, given the radii of the rings, the weights of the spatial filter can be selected in order to improve the rejection of low spatial frequencies, thus increasing spatial selectivity. The theoretical transfer functions of concentric systems are obtained and compared with those of other detection systems. Signals detected with the ring electrodes and with traditional one-dimensional and 2-D systems are compared. The concentric-ring systems show higher spatial selectivity with respect to the traditional detection systems and reduce the problem of electrode location since they are invariant to rotations. The results shown are very promising for the noninvasive detection of single motor unit (MU) activities and decomposition of the surface EMG signal into the constituent MU action potential trains.