Calculation and registration of the same motor unit action potential

Griep, P.A.M.; Gielen, F.L.H.; Boom, H.B.K.; Boon, Kathy; Hoogstraten, L.L.W.; Pool, C.W.; Jonge, W. Wallinga-de

doi:10.1016/0013-4694(82)90004-9

Cited by 60 publications

(27 citation statements)

References 21 publications

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“…Some researchers have modeled the action potential as a simple dipole [4], [15], and many groups have used a balanced tripole [5], [8], [9], [11], [19], [28], whereas others [6], [7], [13], [14], [20], [29] have used the more realistic analytic waveform described by Rosenfalk [30] and modified by Nandedkar and Stalberg [31]. The electric field will fall off more rapidly with a balanced tripole than with a dipole, resulting in less crosstalk around the limb.…”

mentioning

confidence: 99%

Finite element modeling of electromagnetic signal propagation in a phantom arm

Kuiken

Stoykov²,

Popović

et al. 2001

IEEE Trans. Neural Syst. Rehabil. Eng.

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Abstract-Improving the control of artificial arms remains a considerable challenge. It may be possible to graft remaining peripheral nerves in an amputated limb to spare muscles in or near the residual limb and use these nerve-muscle grafts as additional myoelectric control signals. This would allow simultaneous control of multiple degrees of freedom (DOF) and could greatly improve the control of artificial limbs. For this technique to be successful, the electromyography (EMG) signals from the nerve-muscle grafts would need to be independent of each other with minimal crosstalk. To study EMG signal propagation and quantify crosstalk, finite element (FE) models were developed in a phantom-arm model. The models were validated with experimental data collected by applying sinusoidal excitations to a phantom-arm model and recording the surface electric potential distribution. There was a very high correlation ( 0 99) between the FEM data and the experimental data, with the error in signal magnitude generally less than 5%. Simulations were then performed using muscle dielectric properties with static, complex, and full electromagnetic solvers. The results indicate that significant displacement currents can develop ( 50% of total current) and that the fall-off of surface signal power varies with how the signal source is modeled.

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mentioning

confidence: 99%

Finite element modeling of electromagnetic signal propagation in a phantom arm

Kuiken

Stoykov²,

Popović

et al. 2001

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…For this reason the interesting frequency range is associated with the frequency components of EMG signals. This range runs from 10 Hz to about 20 kHz (GATH and STALBERG, 1975;ANDREASSEN and ROSENFALCK, 1978;GRIEP et al, 1982). Our experiments with respect to the determination of electrical conductivities were carried out in a frequency range between 10 Hz and 100 kHz.…”

Section: Introductionmentioning

confidence: 99%

“…In a first approach to obtain a quantitative description of the motor unit action potential (MUAP) (GRIEP et al,, 1982) it turned out that some parameter values show such a large spread in the literature that no unique quantitative MUAPs could be obtained. An example of such a parameter is the electrical conductivity of skeletal muscle tissue.…”

Section: Introductionmentioning

confidence: 99%

Electrical conductivity of skeletal muscle tissue: Experimental results from different musclesin vivo

Gielen

Jonge

Boon

1984

Med. Biol. Eng. Comput.

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116

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“…Modelling of the generation, propagation and extinction of the intracellular action potential has been performed in the past following different approaches (DIMITROV and DIMITROVA, 1998;DIMITROVA, 1974;GOOTZEN et al, 1991;GRIEP et al, 1982;MCGILL and HUYNH, 1988). The general assumption is that the integral of the current density over the muscle fibre length is zero in each instant of time.…”

Section: Description Of the Sourcementioning

confidence: 99%

“…Different approaches have been proposed for the description of the source, including generation, travelling and extinction phenomena (DIMITROV and DIMITROVA, 1998;DIMITROVA, 1974;DIMITROVA et al, 2001;GOOTZEN et al, 1991;GRIEP et al, 1982;GYDIKOV et al, 1986;KLEINPENN~NG et al, 1990;MCGILL and HUYNH, 1988;PLONSEY, 1977). Steps (b) and (c) relate to the description of the tissues separating the sources from the detecting electrodes.…”

Section: Introductionmentioning

confidence: 98%

Advances in surface electromyographic signal simulation with analytical and numerical descriptions of the volume conductor

Farina

Mesin

Martina

2004

Med. Biol. Eng. Comput.

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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.

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Calculation and registration of the same motor unit action potential

Cited by 60 publications

References 21 publications

Finite element modeling of electromagnetic signal propagation in a phantom arm

Finite element modeling of electromagnetic signal propagation in a phantom arm

Electrical conductivity of skeletal muscle tissue: Experimental results from different musclesin vivo

Advances in surface electromyographic signal simulation with analytical and numerical descriptions of the volume conductor

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