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

Electrocorticographic signal classification based on time-frequency decomposition and nonparametric statistical modeling

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

3 Author(s)
Dat, T.H. ; Neural Signal Process. Lab., Inst. for Infocomm Res., Singapore ; Shue, L. ; Cuntai Guan

In this paper, we propose a novel statistical framework based on time-frequency decomposition and nonparametric modelling of electrocortical (ECoG) signals in the context of a Brain Computer Interface. The proposed method decomposes the ECoG signals into subbands (with no down-sampling) using Gabor filters. The subband signals are then encoded using a nonparametric statistical modeling and the distance between the resulting empirical distributions is as used as the classification criterion. Cross-validation experiments were carried out to pre-select the channel (from the multi-channel sources) and subbands which can archive the best classification scores. The proposed framework has been evaluated using Data Set I from the BCI Competition III and results indicate a superiority over conventional vector quantization method particularly when the number of training samples is small. It was found that the proposed nonparametric distribution modeling based on empirical inverse cumulative distribution distance is fast, robust and applicable to the mobile systems

Published in:

Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE

Date of Conference:

Aug. 30 2006-Sept. 3 2006

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.