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On the use of wavelet denoising and spike sorting techniques to process electroneurographic signals recorded using intraneural electrodes

Citi, L and Carpaneto, J and Yoshida, K and Hoffmann, KP and Koch, KP and Dario, P and Micera, S (2008) 'On the use of wavelet denoising and spike sorting techniques to process electroneurographic signals recorded using intraneural electrodes.' Journal of Neuroscience Methods, 172 (2). 294 - 302. ISSN 0165-0270

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Abstract

Among the possible interfaces with the peripheral nervous system (PNS), intraneural electrodes represent an interesting solution for their potential advantages such as the possibility of extracting spikes from electroneurographic (ENG) signals. Their use could increase the precision and the amount of information which can be detected with respect to other processing methods. In this study, in order to verify this assumption, thin-film longitudinal intrafascicular electrodes (tfLIFE) were implanted in the sciatic nerve of rabbits. Various sensory stimuli were applied to the hind limb of the animal and the elicited ENG signals were recorded usin g the tfLIFEs. These signals were processed to determine whether the different types of information can be decoded. Signals were wavelet denoised and spike sorted. Support vector machines were trained to use the spike waveforms found to infer the stimulus applied to the rabbit. This approach was also compared with previously used ENG-processing methods. The results indicate that the combination of wavelet denoising and spike sorting techniques can increase the amount of information extractable from ENG signals recorded with intraneural electrodes. This strategy could allow the development of more effective closed-loop neuroprostheses and hybrid bionic systems connecting the human nervous system with artificial devices. © 2007 Elsevier B.V. All rights reserved.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 13 Mar 2014 12:34
Last Modified: 28 Nov 2017 19:15
URI: http://repository.essex.ac.uk/id/eprint/8779

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