EEG signal classification based on PCA and NN

Author

Changmok Oh ; Kim, Min-Soeng ; Lee, Ju-Jang

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., KAIST, Daejeon

fYear

2006

Firstpage

1848

Lastpage

1851

Abstract

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

Keywords

electroencephalography; medical signal processing; neural nets; principal component analysis; signal classification; time series; EEG image signal classification; PCA; brain computer interface; electroencephalogram pattern classification; multivariate time series data; neural network; principal component analysis; Biological neural networks; Brain; Covariance matrix; Electroencephalography; Electronic mail; Frequency; Neural networks; Pattern classification; Principal component analysis; Sleep; Principal component analysis; electroencephalogram; neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

SICE-ICASE, 2006. International Joint Conference

Conference_Location

Busan

Print_ISBN

89-950038-4-7

Electronic_ISBN

89-950038-5-5

Type

conf

DOI

10.1109/SICE.2006.315801

Filename

4108984

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1618274