Close category search window
 

Estimated generalized least squares electromagnetic source analysis based on a parametric noise covariance model [EEG/MEG]

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

4 Author(s)
Waldorp, L.J. ; Dept. of Psychol., Amsterdam Univ., Netherlands ; Huizenga, H.M. ; Dolan, C.V. ; Molenaar, P.C.M.

Estimated generalized least squares (EGLS) electromagnetic source analysis is used to downweight noisy and correlated data. Standard EGLS requires many trials to accurately estimate the noise covariances and, thus, the source parameters. Alternatively, the noise covariances can be modeled parametrically. Only the parameters of the model describing the noise covariances need to be estimated and, therefore, less trials are required. This method is referred to as parametric EGLS (PEGLS). In this paper, PEGLS is developed and its performance is tested in a simulation study and in a pseudoempirical study.

Published in:
Biomedical Engineering, IEEE Transactions on  (Volume:48 ,  Issue: 6 )

Date of Publication: June 2001

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 2013 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.