A Log-Linearized Gaussian Mixture Network and Its Application to EEG Pattern Classification
IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews Volume 29 Issue 1
Page 60-72
published_at 1999
アクセス数 : 1155 件
ダウンロード数 : 243 件
今月のアクセス数 : 1 件
今月のダウンロード数 : 1 件
この文献の参照には次のURLをご利用ください : https://ir.lib.hiroshima-u.ac.jp/00014192
File |
IEEE_TSMC_C_AR_29_1_60-72_1999.pdf
482 KB
種類 :
fulltext
|
Title ( eng ) |
A Log-Linearized Gaussian Mixture Network and Its Application to EEG Pattern Classification
|
Creator |
Fukuda Osamu
Ichinobe Hiroyuki
Kaneko Makoto
|
Source Title |
IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews
|
Volume | 29 |
Issue | 1 |
Start Page | 60 |
End Page | 72 |
Abstract |
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.
|
Keywords |
Electroencephalography
feedforward neural networks
pattern classification
recurrent nerual networks
|
NDC |
Mechanical engineering [ 530 ]
|
Language |
eng
|
Resource Type | journal article |
Publisher |
IEEE
|
Date of Issued | 1999 |
Rights |
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.
|
Publish Type | Version of Record |
Access Rights | open access |
Source Identifier |
[ISSN] 1094-6977
[DOI] 10.1109/5326.740670
[NCID] AA11198437
[DOI] http://dx.doi.org/10.1109/5326.740670
|