SHERP

Self-Organizing Map을 이용한 고잡음 신경신호의 클러스터링
Clustering of Extracellular Neural Signal With High Background Niose Using Self-Organizing Map

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Authors
김경환; 김성준
Issue Date
1999
Citation
대한의용생체공학회 추계학술대회, 1999, 서울
Abstract
We present a result on unsupewised
classification of extracellular neural signal with
low signal-to-noise ratio (SNR) usrng selforganizing
map {SUM). Instead of using simple
amplitude thresholding, energy operator is used
for the detection of neural spike to utilize both the
instantaneous amplitude and frequency
information. M e n applied to synthesized and real
experimental data with high background noise,
satisfactory clustering can be obtained. If
necessary, learnxng time can be significantly
reduced by using principal components of input
data as the input to the map.
Language
Korean
URI
http://hdl.handle.net/10371/7528
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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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