SHERP

Neural Spike Sorting Under Nearly 0 dB Signal-to-Noise Ratio Using Nonlinear Energy Operator and Artificial Neural-Network Classifier

Cited 0 time in webofscience Cited 159 time in scopus
Authors
Kim, Kyung Hwan; Kim, Sung June
Issue Date
2000-10
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
IEEE Trans. Biomed. Eng., vol. 47, pp. 1406-1411, Oct. 2000
Keywords
neural-network classifierneural spike sortingExtracellular recordingnonlinear energy operatorsignal-to-noise ratio
Abstract
We report a result on neural spike sorting under conditions
where the signal-to-noise ratio is very low. The use of nonlinear energy operator
enables the detection of an action potential, even when the SNR is so
poor that a typical amplitude thresholding method cannot be applied. The
superior detection ability facilitates the collection of a training set under
lower SNR than that of the methods which employ simple amplitude thresholding.
Thus, the statistical characteristics of the input vectors can be better
represented in the neural-network classifier. The trained neural-network
classifiers yield the correct classification ratio higher than 90% when the
SNR is as low as 1.2 (0.8 dB) when applied to data obtained from extracellular
recording from Aplysia abdominal ganglia using a semiconductor
microelectrode array.
ISSN
0018-9294
Language
English
URI
http://hdl.handle.net/10371/8865
DOI
https://doi.org/10.1109/10.871415
Files in This Item:
Appears in Collections:
College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Electrical and Computer Engineering (전기·정보공학부)Journal Papers (저널논문_전기·정보공학부)
  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse