By Topic

EEG feature extraction and classification using data dimension reduction

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
So-Youn Park ; Dept. of Electr. Eng. & Comput. Sci., KAIST, Seoul ; Ju-Jang Lee

Analysis of biological signal plays a very important role in Brain Computer Interface (BCI). Particularly, with electroencephalogram (EEG), we can know the intension or mental state of human. To recognize those features, various parametric feature extraction methods such as central frequency, relative percent spectral energy band (RPEB), etc. is needed. In this paper, we propose an EEG signal classifier which handles time-domain EEG signal as a feature vector and reduces data dimension to create lower dimension features using in the classifier. We believe that the proposed method gives more reliable results than existing ones.

Published in:

Industrial Informatics, 2008. INDIN 2008. 6th IEEE International Conference on

Date of Conference:

13-16 July 2008