EEG signal classification based on simple random sampling technique with least square support vector machine
by Siuly; Yan Li; Peng Wen
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 7, No. 4, 2011

Abstract: This paper proposes a new approach based on Simple Random Sampling (SRS) technique with Least Square Support Vector Machine (LS-SVM) to classify two-class of electroencephalogram (EEG) signals. The experiments are carried out on two EEG databases and a synthetic Ripley dataset. All two-class pairs are tested and our proposed approach obtains a 95.58% average classification accuracy for the EEG epileptic database, 98.73% for the mental imagery tasks EEG database and 100% for Ripley data. We compare our method with two most recent methods for the epileptic database. Experimental results demonstrate that the proposed method is more promising than previously reported classification techniques.

Online publication date: Wed, 21-Jan-2015

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biomedical Engineering and Technology (IJBET):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com