1,722,175 research outputs found
Advances in surface EMG signal simulation with analytical and numerical descriptions of the volume conductor
Full text linkSurface electromyographic (EMG) signal modeling is important for signal interpretation, testing of processing algorithms, detection system design, and didactic purposes. Various surface EMG signal models have been proposed in the literature. In this study we focus on 1) the proposal of a method for modeling surface EMG signals by either analytical or numerical descriptions of the volume conductor for space-invariant systems, and 2) the development of advanced models of the volume conductor by numerical approaches, accurately describing not only the volume conductor geometry, as mainly done in the past, but also the conductivity tensor of the muscle tissue. For volume conductors that are space-invariant in the direction of source propagation, the surface potentials generated by any source can be computed by one-dimensional convolutions, once the volume conductor transfer function is derived (analytically or numerically). Conversely, more complex volume conductors require a complete numerical approach. In a numerical approach, the conductivity tensor of the muscle tissue should be matched with the fiber orientation. In some cases (e.g., multi-pinnate muscles) accurate description of the conductivity tensor may be very complex. A method for relating the conductivity tensor of the muscle tissue, to be used in a numerical approach, to the curve describing the muscle fibers is presented and applied to representatively investigate a bi-pinnate muscle with rectilinear and curvilinear fibers. The study thus propose an approach for surface EMG signal simulation in space invariant systems as well as new models of the volume conductor using numerical methods
Estimation of Muscle Fiber Conduction Velocity from Simulated Surface EMG in Pinnate Muscles
Full text linkUdgivelsesdato: MAR 1
An analytical model for surface EMG generation in volume conductors with smooth conductivity variations
Full text linkUdgivelsesdato: MA
Estimating motor unit discharge pattern from the surface electromyogram
Full text linkUdgivelsesdato: MA
Simulation of surface EMG signals generated by muscle tissues with in-homogeneity due to fiber pinnation
Full text linkSurface electromyographic (EMG) signal modeling has important applications in the interpretation of experimental EMG data. Most models of surface EMG generation considered volume conductors homogeneous in the direction of propagation of the action potentials. However, this may not be the case in practice due to local tissue inhomogeneities or to the fact that there may be groups of muscle fibers with different orientations. This study addresses the issue of analytically describing surface EMG signals generated by bi-pinnate muscles, i.e., muscles which have two groups of fibers with two orientations. The approach will also be adapted to the case of a muscle with fibers inclined in the depth direction. Such muscle anatomies are inhomogeneous in the direction of propagation of the action potentials with the consequence that the system can not be described as space invariant in the direction of source propagation. In these conditions, the potentials detected at the skin surface do not travel without shape changes. This determines numerical issues in the implementation of the model which are addressed in this work. The study provides the solution of the nonhomogenous, anisotropic problem, proposes an implementation of the results in complete surface EMG generation models (including finite-length fibers), and shows representative results of the application of the models proposed
Methods for estimating muscle fibre conduction velocity from surface electromyographic signals
No full textAccessing the neural drive to muscle and translation to neurorehabilitation technologies
No full textFrom Man to Machine: Neural Control of Fine Hand Movements
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197545.pdf (Publisher’s version ) (Open Access)Radboud University, 14 december 2018Promotores : Stegeman, D.F., Farina, D., Opstal, A.J. van Co-promotor : Maas, H.163 p
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