Title :
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
fDate :
6/1/2001 12:00:00 AM
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;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
