By Topic

Adaptive EEG signal classification using stochastic approximation methods

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)
Shiliang Sun ; Dept. of Comput. Sci. & Technol., East China Normal Univ., Shanghai ; Man Lan ; Yue Lu

Classification of time-varying electrophysiological signals is an important problem in the development of brain-computer interfaces (BCIs). Designing adaptive classifiers is a potential way to address this task. In this paper, Bayesian classifiers with Gaussian mixture models (GMMs) are adopted as the decision rule to classify electroencephalogram (EEG) signals. The stochastic approximation method (SAM) is used as the specific gradient descent method for updating the parameters of mean values and covariance matrices in the distribution of GMMs, where the parameters are simultaneously updated in a batch mode. Experimental results using data from a BCI show that the stochastic approximation method is effective for EEG classification tasks.

Published in:

Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on

Date of Conference:

March 31 2008-April 4 2008