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Self-Organizing Map을 이용한 고잡음 신경신호의 클러스터링 : Clustering of Extracellular Neural Signal With High Background Niose Using Self-Organizing Map
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김경환 | - |
dc.contributor.author | 김성준 | - |
dc.date.accessioned | 2009-08-24 | - |
dc.date.available | 2009-08-24 | - |
dc.date.issued | 1999 | - |
dc.identifier.citation | 대한의용생체공학회 추계학술대회, 1999, 서울 | en |
dc.identifier.uri | https://hdl.handle.net/10371/7528 | - |
dc.description.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. | en |
dc.description.sponsorship | 본 연구는 보건복지부에서 시행한 '98 보건의료기술연구개발사업의 결과입니다. | en |
dc.language.iso | ko | en |
dc.title | Self-Organizing Map을 이용한 고잡음 신경신호의 클러스터링 | en |
dc.title.alternative | Clustering of Extracellular Neural Signal With High Background Niose Using Self-Organizing Map | en |
dc.type | Conference Paper | en |
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