Multiple-electrode nerve cuffs for low-velocity and velocity-selective neural recording.
MED BIOL ENG COMPUT
634 - 643.
In the paper, a method using multiple-electrode nerve cuffs is presented that enables electroneurographic signals (ENG) to be recorded selectively by action potential velocity. The theory uses a one-dimensional model of the electrodes in the cuff. Using this model, the transfer function for a single tripole is derived, and it is shown that more than one tripole signal can be recorded from within a cuff. When many tripole signals are available and are temporally aligned by artificial delays and summed, there is a significant increase in the amplitude of the recorded action potential, depending on the cuff length and the action potential velocity, with the greatest gain occurring for low velocities. For example, a cuff was considered that was constrained by surgical considerations to 30 mm between the end electrodes. For action potentials with a velocity of 120 ms(-1), it was shown that, as the number of tripoles increased from one, the peak energy spectral density of the recorded output increased by a factor of about 1.6 with three tripoles, whereas, for 20 ms(-1), the increase was about 19, with ten tripoles. The time delays and summation act as a velocity-selective filter. With consideration of the energy spectral densities at frequencies where these are maximum (to give the best signal-to-noise ratio), the tuning curves are presented for these velocity-selective filters and show that useful velocity resolution is possible using this method. For a 30 mm cuff with nine tripoles, it is demonstrated that it is possible to resolve at least five distinct velocity bands in the range 20-120 ms(-1).
|Title:||Multiple-electrode nerve cuffs for low-velocity and velocity-selective neural recording|
|Keywords:||nerve cuff, tripolar recording, multi-electrode cuff velocity selectivity, small fibre recording, FUNCTIONAL ELECTRICAL-STIMULATION, SURFACE MYOELECTRIC SIGNALS, MYELINATED NERVE, PERONEAL NERVE, ACTION-POTENTIALS, PERIPHERAL-NERVE, INFORMATION, MODEL, CONDUCTION, FOOTDROP|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Medical Physics and Bioengineering
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