The objective is to improve the performance of surgically-implanted peripheral nerve recording so that more useful information can be extracted from the neural traffic. The output of such systems consists of the mixed responses to both motor and sensory fibres, and to fibres of all diameters. Professor John Taylor et al. have invented a method for selective recording which allows nerve signals to be classified by conduction velocity (and therefore fibre diameter), in addition to improving the signal-to-noise ratio compared to conventional (tripolar) cuff. The method uses a Multi-electrode Cuff (MEC) and linear signal processing. It is expected to have many applications in neuroprosthetics.
To show that the system does distinguish signals of different function that are carried in fibres of different diameter, it is to be tested by in-vivo experiments in animals. Acute experiments will test the understanding with compound action potentials (CAPs) and then naturally-occurring neural signals after surgical preparation and under several well-defined experimental conditions.
This thesis presents the successful application of conduction velocity-selective ENG recording system to electrically evoked potentials using the frog sciatic nerve. But when it came to testing the recording system on naturally-occurring neural signals from the pig, time constraints and equipment malfunction did not allow for successful recordings. However, after extensive re-designing of the equipment and experiment set-up, a lot has been learnt when it comes to mounting a pig’s limb to a test rig and the mechanism behind this tri-segmented limb. The final design of the apparatus will surely be a good continuation for further research in this area.
|Date of Award||1 May 2008|
|Supervisor||John Taylor (Supervisor)|