Advances in surface electromyographic signal simulation with analytical and numerical descriptions of the volume conductor.
Surface electromyographic (EMG) signal modelling 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. This study focuses on the proposal of a method for modelling surface EMG signals, using either analytical or numerical descriptions of the volume conductor for space-invariant systems, and on the development of advanced models of the volume conductor by numerical approaches, accurately describing the volume conductor geometry and the conductivity, 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 has been 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 fibre orientation. In some cases (e.g. multi-pinnate muscles), accurate description of the conductivity tensor can 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 fibres is presented and applied to investigate representatively a bi-pinnate muscle with rectilinear and curvilinear fibres. The study thus proposes an approach for surface EMG signal simulation in space invariant systems, as well as new models of the volume conductor using numerical methods.
Farina
D
D
Dipartimento di Elettronica, Laboratorio di Ingegneria del Sistema Neuromuscolare, Politecnico di Torino, Torino, Italy. dario.farina@polito.it
Mesin
L
L
Martina
S
S
D000200:Action Potentials / Q000502:physiology; D004553:Electric Conductivity; D004576:Electromyography / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D008959:Models, Neurological; D012815:Signal Processing, Computer-Assisted