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.
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 language | English |
|---|---|
| Pages (from-to) | 401-410 |
| Number of pages | 11 |
| Journal | IEEE Transactions on Biomedical Circuits and Systems |
| Volume | 8 |
| Issue number | 3 |
| Early online date | 25 Oct 2013 |
| DOIs | |
| Publication status | Published - 2013 |
Keywords
- Artificial neural networks
- biomedical signal processing
- biomedical transducers
- microelectronic implants
- neural prosthesis