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

Author

Waldorp, Lourens J. ; Huizenga, Hilde M. ; Dolan, Conor V. ; Molenaar, Peter C M

Author_Institution

Dept. of Psychol., Amsterdam Univ., Netherlands

Volume

48

Issue

6

fYear

2001

fDate

6/1/2001 12:00:00 AM

Firstpage

737

Lastpage

741

Abstract

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.

Keywords

brain models; electroencephalography; magnetoencephalography; noise; EEG/MEG noise covariance; estimated generalized least squares electromagnetic source analysis; model parameters; parametric noise covariance model; pseudoempirical study; simulation study; Biomedical measurements; Covariance matrix; Electroencephalography; Electromagnetic analysis; Electromagnetic interference; Least squares approximation; Least squares methods; Parameter estimation; Psychology; Working environment noise; Chi-Square Distribution; Computer Simulation; Electroencephalography; Humans; Least-Squares Analysis; Magnetoencephalography; Mathematics; Signal Processing, Computer-Assisted; Statistics, Nonparametric;

fLanguage

English

Journal_Title

Biomedical Engineering, IEEE Transactions on

Publisher

ieee

ISSN

0018-9294

Type

jour

DOI

10.1109/10.923793

Filename

923793