PCA+HMM+SVM for EEG pattern classification

Author

Lee, Hyekyung ; Cho, Seungjin

Author_Institution

Dept. of Comput. Sci. & Eng., POSTECH, South Korea

Volume

1

fYear

2003

fDate

1-4 July 2003

Firstpage

541

Abstract

Electroencephalogram (EEG) pattern classification plays an important role in the domain of brain computer interface (BCI). Hidden Markov model (HMM) might be a useful tool in EEG pattern classification since EEG data is a multivariate time series data which contains noise and artifacts. In this paper we present methods for EEG pattern classification which jointly employ principal component analysis (PCA) and HMM. Along this line, two methods are introduced: (1) PCA+HMM; (2) PCA+HMM+SVM. Usefulness of principal component features and our hybrid method is confirmed through the classification of EEG that is recorded during the imagination of a left or right hand movement.

Keywords

computer interfaces; electroencephalography; feature extraction; hidden Markov models; noise; pattern classification; principal component analysis; support vector machines; EEG pattern classification; PCA+HMM+SVM; brain computer interface; electroencephalogram; hidden Markov model; left hand movement imagination; multivariate time series data; principal component analysis; right hand movement imagination; support vector machine; Brain modeling; Covariance matrix; Eigenvalues and eigenfunctions; Electroencephalography; Hidden Markov models; Linear discriminant analysis; Pattern classification; Principal component analysis; Sequences; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Its Applications, 2003. Proceedings. Seventh International Symposium on

Print_ISBN

0-7803-7946-2

Type

conf

DOI

10.1109/ISSPA.2003.1224760

Filename

1224760