Research Repository

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 9780470916735

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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: 30 Mar 2021 19:15

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