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Pattern recognition of nerve signals using an artificial neural network

Montelius, L. LU ; Sebelius, F. LU orcid ; Eriksson, L. ; Holmberg, H. ; Schouenborg, J. LU orcid ; Danielsen, N. LU ; Wallman, L. LU ; Laurell, T. LU and Balkenius, C. LU orcid (1996) 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society p.1502-1503
Abstract

By using a microfabricated nerve chip with integrated electrodes through which peripheral nerves can generate and make electrical connects, it should be possible to control a remote prosthesis by processing the detected nerve signals. In this study different artificial neural networks have been employed for classification of such complex patterns of signals. The signals were obtained from four electrodes detecting muscle activity in a rat hindlimb as a consequence of applied stimulus to the rat right hindpaw. These signals recorded at four different sites resembles a situation of a nerve chip with four electrodes, which implies that we might be able to use the same strategy when analyzing data from a four-electrode chip to obtain... (More)

By using a microfabricated nerve chip with integrated electrodes through which peripheral nerves can generate and make electrical connects, it should be possible to control a remote prosthesis by processing the detected nerve signals. In this study different artificial neural networks have been employed for classification of such complex patterns of signals. The signals were obtained from four electrodes detecting muscle activity in a rat hindlimb as a consequence of applied stimulus to the rat right hindpaw. These signals recorded at four different sites resembles a situation of a nerve chip with four electrodes, which implies that we might be able to use the same strategy when analyzing data from a four-electrode chip to obtain information from the nervous system. In this paper we will address the usefulness of different network topologies for analyzing measured in-vivo data from an implanted perforated nerve chip.

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author
; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to conference
publication status
published
subject
pages
1502 - 1503
conference name
18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
conference location
Amsterdam, Neth
conference dates
1996-10-31 - 1996-11-03
external identifiers
  • scopus:0030314376
language
English
LU publication?
yes
id
db1cda87-56ec-494d-8867-0ac32af52f02
date added to LUP
2019-06-24 15:51:22
date last changed
2024-01-03 14:25:52
@misc{db1cda87-56ec-494d-8867-0ac32af52f02,
  abstract     = {{<p>By using a microfabricated nerve chip with integrated electrodes through which peripheral nerves can generate and make electrical connects, it should be possible to control a remote prosthesis by processing the detected nerve signals. In this study different artificial neural networks have been employed for classification of such complex patterns of signals. The signals were obtained from four electrodes detecting muscle activity in a rat hindlimb as a consequence of applied stimulus to the rat right hindpaw. These signals recorded at four different sites resembles a situation of a nerve chip with four electrodes, which implies that we might be able to use the same strategy when analyzing data from a four-electrode chip to obtain information from the nervous system. In this paper we will address the usefulness of different network topologies for analyzing measured in-vivo data from an implanted perforated nerve chip.</p>}},
  author       = {{Montelius, L. and Sebelius, F. and Eriksson, L. and Holmberg, H. and Schouenborg, J. and Danielsen, N. and Wallman, L. and Laurell, T. and Balkenius, C.}},
  language     = {{eng}},
  pages        = {{1502--1503}},
  title        = {{Pattern recognition of nerve signals using an artificial neural network}},
  year         = {{1996}},
}