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

P300 based brain-computer interface using Hidden Markov Models

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.

The purchase and pricing options are temporarily unavailable. Please try again later.
4 Author(s)
Helmy, S. ; Dept. of Ind. Electron. & Control, Menoufia Univ., Menouf ; Al-ani, T. ; Hamam, Y. ; El-madbouly, E.

This paper reports on preliminary work on the use of hidden Markov models (HMMs) approach for tasks classification in P300-based brain-computer interface (BCI) system. Every HMM is trained on a set of electroencephalogram (EEG) records issued from different sessions corresponding to the same task. The HMMs that has been built take into account the variability of EEGs during different sessions. Based on Bayesian inference criterion (BIC), the proposed HMM training algorithm is able to select the optimal number of states corresponding to each set of EEG training records. For every state number, each iteration is initialized by the most appropriate model using data clustering, and by the rejection of the least probable state of the previous iteration. Consequently, every training iteration begin by a more precise model. We report training procedures and validation results of the models. The obtained results give a correct and promising classification rates for all subjects which is the objective of this work.

Published in:

Intelligent Sensors, Sensor Networks and Information Processing, 2008. ISSNIP 2008. International Conference on

Date of Conference:

15-18 Dec. 2008

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.