Title :
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
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;
Conference_Titel :
SICE-ICASE, 2006. International Joint Conference
Conference_Location :
Busan
Print_ISBN :
89-950038-4-7
Electronic_ISBN :
89-950038-5-5
DOI :
10.1109/SICE.2006.315801
