EEG signal classification based on PCA and NN

被引：0

作者：

Oh, Changmok ^{[1
]}

Kim, Min-Soeng ^{[1
]}

Lee, Ju-Jang ^{[1
]}

机构：

[1] Dept Elect Engn & Comp Sci, Taejon, South Korea

来源：

2006 SICE-ICASE INTERNATIONAL JOINT CONFERENCE, VOLS 1-13 | 2006年

关键词：

principal component analysis; neural network; electroencephalogram;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Electroencephalogram (EEG) pattern classification plays an important role in the domain of brain computer interface. However, EEG data is a multivariate time series data which contains noise and artifacts. In this paper we present methods contains for EEG pattern classification which jointly employ principal component analysis (PCA) and neural networks (NN). We believe that this hybrid approach offers the better chance for reliable classification of the EEG signal.

引用

页码：4760 / +

页数：3

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