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ID 14192
本文ファイル
著者
Fukuda, Osamu
Ichinobe, Hiroyuki
Kaneko, Makoto
キーワード
Electroencephalography
feedforward neural networks
pattern classification
recurrent nerual networks
NDC
機械工学
抄録(英)
The present paper proposes a new probabilisticneural network (NN) that can estimate a posteriori probabilityfor a pattern classification problem. The structure of the proposednetwork is based on a statistical model composed by a mixtureof log-linearized Gaussian components. However, the forwardcalculation and the backward learning rule can be defined in thesame manner as the error backpropagation NN. In this paper, theproposed network is applied to the electroencephalogram (EEG)pattern classification problem. In the experiments, two types of aphotic stimulation, which are caused by eye opening/closing andartificial light, are used to collect the data to be classified. It isshown that the EEG signals can be classified successfully andthat the classification rates change depending on the number oftraining data and the dimension of the feature vectors.
掲載誌名
IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews
29巻
1号
開始ページ
60
終了ページ
72
出版年月日
1999
出版者
IEEE
ISSN
1094-6977
NCID
出版者DOI
言語
英語
NII資源タイプ
学術雑誌論文
広大資料タイプ
学術雑誌論文
DCMIタイプ
text
フォーマット
application/pdf
著者版フラグ
publisher
権利情報
Copyright (c) 1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
関連情報URL
部局名
工学研究科