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Spatiotemporal EEG/MEG source analysis based on a parametric noise covariance model
 IEEE Transactions on Biomedical Engineering
, 2002
"... c○2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other w ..."
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Cited by 22 (4 self)
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c○2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Model selection in electromagnetic source analysis with an application to VEF’s
 IEEE Transactions on Biomedical Engineering
, 2002
"... Abstract — In electromagnetic source analysis it is necessary to determine how many sources are required to describe the EEG or MEG adequately. Model selection procedures (MSP’s, or goodness of fit procedures) give an estimate of the required number of sources. Existing and new MSP’s are evaluated i ..."
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Abstract — In electromagnetic source analysis it is necessary to determine how many sources are required to describe the EEG or MEG adequately. Model selection procedures (MSP’s, or goodness of fit procedures) give an estimate of the required number of sources. Existing and new MSP’s are evaluated in different source and noise settings: two sources which are close or distant, and noise which is uncorrelated or correlated. The commonly used MSP residual variance is seen to be ineffective, that is it often selects too many sources. Alternatives like the adjusted Hotelling’s test, Bayes information criterion, and the Wald test on source amplitudes are seen to be effective. The adjusted Hotelling’s test is recommended if a conservative approach is taken, and MSP’s such as Bayes information criterion or the Wald test on source amplitudes are recommended if a more liberal approach is desirable. The MSP’s are applied to empirical data (visual evoked fields). I.
Frequency domain simultaneous source and source coherence estimation with an application to MEG
 IEEE Trans. on Biomedical Engineering
, 2004
"... c○2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other w ..."
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Cited by 3 (2 self)
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c○2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained
Exchange Algorithms for Constructing ModelRobust Experimental Designs
, 2009
"... Optimal experimental design procedures, utilizing criteria such as Doptimality, are useful for producing experimental designs for quantitative responses, often under nonstandard conditions such as constrained design spaces. However, these methods require a priori knowledge of the exact form of the ..."
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Optimal experimental design procedures, utilizing criteria such as Doptimality, are useful for producing experimental designs for quantitative responses, often under nonstandard conditions such as constrained design spaces. However, these methods require a priori knowledge of the exact form of the response function, an often unrealistic assumption. Modelrobust designs are those which, from our perspective, are efficient with respect to a set of possible models. In this paper, we develop a modelrobust technique which, when the possible models are nested, is Doptimal with respect to an associated multiresponse model. In addition to providing a justification for the procedure, this motivates the generalization of a modified Fedorov exchange algorithm, which is developed and used to construct exact modelrobust designs. We give several examples and compare our designs with two modelrobust procedures in the literature.
Stochastic maximum likelihood mean and crossspectrum structure estimation: analytic and neuromagnetic Monte Carlo results
, 2004
"... In [1] we proposed to analyze crossspectrum matrices obtained from electro or magnetoencephalographic (EEG/MEG) signals, to obtain estimates of the EEG/MEG sources and their coherence. In this paper we extend this method in two ways. First, by modelling such interactions as linear filters, and se ..."
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In [1] we proposed to analyze crossspectrum matrices obtained from electro or magnetoencephalographic (EEG/MEG) signals, to obtain estimates of the EEG/MEG sources and their coherence. In this paper we extend this method in two ways. First, by modelling such interactions as linear filters, and second, by taking the mean of the signals across different trials into account. To obtain estimates we propose a stochastic maximum likelihood (SML) method, and obtain the concentrated likelihood that includes the trial means.
Index Terms Stochastic maximum likelihood, SML algorithm, concentrated likelihood, singular Hessian
, 2005
"... Maximum likelihood estimation of emitter source parameters in array signal processing for stochastic source signals is well established in the literature. Currently available results in the literature however, have relied on the assumption that the array response matrix has full column rank. In cert ..."
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Maximum likelihood estimation of emitter source parameters in array signal processing for stochastic source signals is well established in the literature. Currently available results in the literature however, have relied on the assumption that the array response matrix has full column rank. In certain models used in for example chemistry, telecommunications, oceanography, and biomedical engineering this is not necessarily the case. In this paper we focus on magnetoencephalography, where the method has been previously proposed in brain function connectivity analysis. Here rank deficiency of the response matrix occurs if spherically symmetric head models for MEG signals are used. In this paper we show that the assumption of a full rank response matrix is unnecessary, which has the advantage that complicating reparameterization is unnecessary. We show that the method of concentrating the likelihood remains valid, including statistical inference from the concentrated likelihood, and generalize a well known algorithm to this case. We exemplify the derived results in simulations.
neuromagnetic
"... Stochastic maximum likelihood mean and crossspectrum structure modelling in ..."
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Stochastic maximum likelihood mean and crossspectrum structure modelling in