TY - GEN

T1 - Maximum likelihood estimation with a parametric noise covariance model for instantaneous and spatio-temporal electromagnetic source analysis

AU - Waldorp, L. J.

AU - Huizenga, H. M.

AU - Dolan, C. V.

AU - Grasman, R. P.P.P.

AU - Molenaar, P. C.M.

N1 - Publisher Copyright:
© 2000 IEEE.

PY - 2000

Y1 - 2000

N2 - 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.

AB - 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.

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U2 - 10.1109/SAM.2000.878011

DO - 10.1109/SAM.2000.878011

M3 - Conference contribution

AN - SCOPUS:84949565109

T3 - Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop

SP - 266

EP - 270

BT - Proceedings of the 2000 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAME 2000

PB - IEEE Computer Society

T2 - IEEE Sensor Array and Multichannel Signal Processing Workshop, SAME 2000

Y2 - 16 March 2000 through 17 March 2000

ER -