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    题名: Extracting the Critical Frequency Bands to Classify Vigilance States of Rats by Using a Novel Feature Selection Algorithm
    作者: Chou, Chien-Hsing;Kuo, Chung-Chih;Yu, Zong-En;Tai1, Hsien-Pang;Chen, Ke-Wei
    贡献者: 淡江大學電機工程學系
    关键词: feature selection;frequency band;pattern recognition;vigilance states
    日期: 2013-02-25
    上传时间: 2013-03-07 16:46:08 (UTC+8)
    摘要: Identifying mammalian vigilance states has recently become an important topic in biological science research. The biological researchers concern not only to improve the accuracy rate for classifying the vigilance states, but also to extract the meaningful frequency bands. In this study, we propose a novel feature selection to extract the critical frequency bands of rat’s EEG signals. The proposed algorithm adopts the concept of neighborhood relation during adding and eliminating a candidate feature. In the experiments, the proposed method shows better accuracy rate, and find out the feature subset which locate on the critical frequency bands for recognizing rat’s vigilance states.
    關聯: 2013 2nd International Conference on Information Computer Application (ICICA 2013), 4p.
    显示于类别:[電機工程學系暨研究所] 會議論文

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