Maximum likelihood estimation with a parametric noise covariance model for instantaneous and spatio-temporal electromagnetic source analysis

L. J. Waldorp, H. M. Huizenga, C. V. Dolan, R. P.P.P. Grasman, Peter Molenaar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In instantaneous encephalogram or magnetoencephalogram (EEG/MEG) source analysis, ordinary least squares estimation (OLS) requires that the spatial noise is homoscedastic and uncorrelated over sensors. In spatio-temporal analysis OLS also requires that the noise is homoscedastic and uncorrelated in time (over samples). Generally, these assumptions are violated and, as a consequence, OLS can give rise to inaccuracies in the estimates of location and moment parameters of sources underlying the EEG/MEG. To improve these estimates of the sources, the generalized least squares (GLS) was developed, which uses the spatial or spatio-temporal noise covariances. In GLS these noise covariances are estimated from trial variation around the mean. Therefore, GLS requires many trials to accurately estimate the spatial noise covariances and thus the source parameters. Alternatively, with maximum likelihood (ML) the spatial or spatio-temporal noise covariances can be modeled parametrically. Here, only the model parameters describing the noise covariances need to be estimated. Consequently, fewer trials are required to obtain accurate noise covariances and consequently accurate source parameters. In this paper ML estimation for spatio-temporal analysis is derived, and it is shown that the noise and source parameters can be estimated separately. Furthermore, the likelihood ratio function is proposed to estimate the spatial or spatio-temporal noise covariance model parameters, which can also be used to test whether the model is adequate.

Original languageEnglish (US)
Title of host publicationProceedings of the 2000 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAME 2000
PublisherIEEE Computer Society
Pages266-270
Number of pages5
ISBN (Electronic)0780363396
DOIs
StatePublished - Jan 1 2000
EventIEEE Sensor Array and Multichannel Signal Processing Workshop, SAME 2000 - Cambridge, United States
Duration: Mar 16 2000Mar 17 2000

Publication series

NameProceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
Volume2000-January
ISSN (Electronic)2151-870X

Other

OtherIEEE Sensor Array and Multichannel Signal Processing Workshop, SAME 2000
CountryUnited States
CityCambridge
Period3/16/003/17/00

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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