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Classification of Neural Spike Sorting Under Nearly 0 dB Signal-to-Noise-Ratio

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Authors
Kim, Kyung Hwan; Kim, Sung June
Issue Date
1999-10
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
1999 BMES -IEEE EMBS Joint Conference, vol. 1, p. 410, Atlanta, USA, Oct. 1999
Keywords
Extracellulr recordingNeural spike sortingSignal-to-noise ratioNonlinear energy operatorNeural network classifier
Abstract
We present neural spike sorting when the signal-to-noise ratio (SNR) is close to 0 dB. The use of nonlinear energy operator enables detection of an action potential even when the SNR is so poor that the usual amplitude thresholding method cannot be applied. Thus training sets that effectively represent the probability distribution of the input vectors can be obtained and the learning capability of the neural network classifiers can be better utilized The trained classifiers exhibit correct classification ratio higher than 90% when the SNR is as low as 1.2 (0.8 dB) when applied to the extracellular recording obtained from Aplysia abdominal ganglion using semiconductor microelectrode array
Language
English
URI
http://hdl.handle.net/10371/8905
DOI
https://doi.org/10.1109/IEMBS.1999.802487
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Electrical and Computer Engineering (전기·정보공학부)Others_전기·정보공학부
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