Improved signal processing methods for velocity selective neural recording using multi-electrode cuffs

Assad Al-Shueli, C T Clarke, Nick Donaldson, J T Taylor

Research output: Contribution to journalArticle

6 Citations (Scopus)
155 Downloads (Pure)

Abstract

This paper describes an improved system for obtaining
velocity spectral information from electroneurogram
recordings using multi-electrode cuffs (MECs). The starting point
for this study is some recently published work that considers
the limitations of conventional linear signal processing methods
(‘delay-and-add’) with and without additive noise. By contrast to
earlier linear methods, the present paper adopts a fundamentally
non-linear velocity classification approach based on a type of
artificial neural network (ANN). The new method provides a
unified approach to the solution of the two main problems of the
earlier delay-and-add technique, i.e., a damaging decline in both
velocity selectivity and velocity resolution at high velocities. The
new method can operate in real-time, is shown to be robust in the
presence of noise and also to be relatively insensitive to the form
of the action potential waveforms being classified.
Original languageEnglish
Pages (from-to)401-410
Number of pages11
JournalIEEE Transactions on Biomedical Circuits and Systems
Volume8
Issue number3
Early online date25 Oct 2013
DOIs
Publication statusPublished - 2013

Keywords

  • Artificial neural networks
  • biomedical signal processing
  • biomedical transducers
  • microelectronic implants
  • neural prosthesis

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