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Wavelet Denoising and Conditioning of Neural Recordings

Citi, L and Micera, S (2013) 'Wavelet Denoising and Conditioning of Neural Recordings.' In: UNSPECIFIED, (ed.) Introduction to Neural Engineering for Motor Rehabilitation. UNSPECIFIED, 173 - 182. ISBN 9781118628522

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Abstract

© 2013 The Institute of Electrical and Electronics Engineers, Inc. All rights reserved. This chapter presents wavelet-based denoising algorithms as a preprocessing stage before spike detection and sorting. The first part of the chapter overviews wavelet-based denoising algorithms. The dyadic wavelet transform is compared with a timeinvariant approach, showing that the latter is best suited to the denoising of neural signals. The second part of the chapter shows a sample application with eletroneurographic (ENG) signals recorded from the sciatic nerve of rabbits while the experimenter stimulated the paw of the animal. The wavelet-based denoising is compared with a traditional band-pass filter in two cases: when followed by spike sorting and when followed by traditional rectified bin integration (RBI). The results illustrate the benefits of wavelet denoising over standard band-pass filtering and demonstrate that there is an even more marked improvement when the subsequent step requires signals with high signal-to-noise ratio (SNR), such as in the case of spike sorting.

Item Type: Book Section
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: 04 Apr 2014 14:41
Last Modified: 17 Aug 2017 17:53
URI: http://repository.essex.ac.uk/id/eprint/8795

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