Motor imagery classification from human EEG signatures
by Rohtash Dhiman; Priyanka; J.S. Saini
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 26, No. 1, 2018

Abstract: Brain-computer interface (BCI) systems translate the imagination in the human brain to an action in real world by means of machines. These systems can prove to be a blessing for severely impaired patients in terms of BCI prosthetics. This paper proposes a motor imagery classification system based upon Wavelet Packet Decomposition (WPD) and Support Vector Machine (SVM). The wavelet packet transform is used for both selection of sensory motor frequency band and feature extraction. The publically available BCI competition-IV data set-I has been used to evaluate the performance of the proposed scheme. The obtained classification results outperform the previously reported results of the technique Noise Assisted Multivariate Empirical Mode Decomposition (NA-MEMD) for subjects 'a' and 'b', where 'a' and 'b' refers to two subjects from BCI competition data set-IV.

Online publication date: Thu, 11-Jan-2018

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