Biomedical subjects

H M Huizenga

Publications and source records attributed to H M Huizenga.

4 recordsLinked to original sources

PubMed2001

Simultaneous MEG and EEG source analysis.

A method is described to derive source and conductivity estimates in a simultaneous MEG and EEG source analysis. In addition the covariance matrix of the estimates is derived. Simulation studies with a concentric spheres model and a more realistic boundary element model indicate that this method has several advantages, even if only a few EEG sensors are added to a MEG configuration. First, a simultaneous analysis profits from the 'preferred' location directions of MEG and EEG. Second, deep sources can be estimated quite accurately, which is an advantage compared to MEG. Third, superficial sources profit from accurate MEG location and from accurate EEG moment. Fourth, the radial source component can be estimated, which is an advantage compared to MEG. Fifth, the conductivities can be estimated. It is shown that conductivity estimation gives a substantial increase in precision, even if the conductivities are not identified appropriately. An illustrative analysis of empirical data supports these findings.

Computer Simulation↗

PubMed2001

Estimated generalized least squares electromagnetic source analysis based on a parametric noise covariance model.

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.

Chi-Square Distribution↗

PubMed1996

Ordinary least squares dipole localization is influenced by the reference.

It is shown empirically, analytically and in simulations that common and average referenced recordings differentially affect the accuracy of equivalent source estimates. This effect is mediated by the influence of the reference on noise correlations. The general conclusion of this analysis is that, if software only allows for ordinary least squares estimation (OLS), then average referencing should be preferred, although these estimates will still be sub-optimal. Optimal estimates are derived by generalized least squares (GLS) which accounts for correlated noise. With GLS, average and common referenced recordings give rise to comparable accuracy.

Brain↗

PubMed1995

Equivalent source estimation of scalp potential fields contaminated by heteroscedastic and correlated noise.

The customary ordinary least squares (OLS) approach to the estimation of equivalent sources of scalp potential fields relies on the assumption that noise in the potential measurements has an equal variance and is uncorrelated over leads. It is shown that this assumption is likely to be violated in practice, for instance by the use of a common reference lead. We describe tests to detect these violations and we propose several versions of an alternative estimation method called iterated generalised least squares (IGLS), which accounts for heteroscedastic or correlated noise by incorporating an estimate of the covariance matrix of the noise derived from single trial OLS residuals. Simulation results indicate that these alternatives give a considerable increase in the accuracy of both the parameter and the standard error and confidence interval estimates. The proposed tests and methods are finally integrated into a stepwise approach to equivalent source estimation, which incorporates in addition a test on the goodness of fit of the model, an assessment of the confidence intervals of the parameters and a powerful test of differences between experimental conditions. This stepwise approach is applied to the modelling of equivalent sources of early visual potentials elicited in a spatial attention task.

Brain↗