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
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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