Cart (Loading....) | Create Account
Close category search window

Real coded GA-based SVM for motor imagery classification in a Brain-Computer Interface

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

4 Author(s)
Bamdadian, A. ; Electr. & Comput. Eng. Dept., Nat. Univ. of Singapore, Singapore, Singapore ; Cuntai Guan ; Kai Keng Ang ; Jianxin Xu

The brain signals are generally measured by Electroencephalogram (EEG) in Brain-Computer Interface (BCI) applications. In motor imagery-based BCI, the performed MI tasks (e.g., imagined hand movement) are identified through a classification algorithm to communicate and control the device. Consequently, improving the performance of the classifier is crucial to the success of the BCI system. One of the most popular linear classifier in BCI applications is the Support Vector Machine (SVM). This paper improves the performance of MI-based BCI by finding the optimum free kernel parameters of the SVM classifier. A real-coded genetic algorithm is utilized to determine the free kernel parameters of the SVM. The performance of this method is evaluated using publicly available BCI Competition IV dataset IIa for right and left hand motor imagery tasks. The results show that using real-valued GA-based SVM with Polynomial or Gaussian kernel improves the average accuracy over nine subjects compared with the baseline (i.e., the grid search method). Hence, using automated method (GA) helps us in improving the performance of the MI-based BCI especially for subjects with poor performance.

Published in:

Control and Automation (ICCA), 2011 9th IEEE International Conference on

Date of Conference:

19-21 Dec. 2011

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.