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14
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 12 (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.
A mathematical approach to the temporal stationarity of background noise in MEG/EEG measurements
 NeuroImage
, 2003
"... The general spatiotemporal covariance matrix of the background noise in MEG/EEG signals is huge. To reduce the dimensionality of this matrix it is modeled as a Kronecker product of a spatial and a temporal covariance matrix. When the number of time samples is larger than, say, J 500, the iterative ..."
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Cited by 7 (3 self)
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The general spatiotemporal covariance matrix of the background noise in MEG/EEG signals is huge. To reduce the dimensionality of this matrix it is modeled as a Kronecker product of a spatial and a temporal covariance matrix. When the number of time samples is larger than, say, J 500, the iterative Maximum Likelihood estimation of these two matrices is still too timeconsuming to be useful on a routine basis. In this study we looked for methods to circumvent this computationally expensive procedure by using a parametric model with subjectdependent parameters. Such a model would additionally help with interpreting MEG/EEG signals. For the spatial covariance, models have been derived already and it has been shown that measured MEG/EEG signals can be understood spatially as random processes, generated by random dipoles. The temporal covariance, however, has not been modeled yet, therefore we studied the temporal covariance matrix in several subjects. For all subjects the temporal covariance shows an alpha oscillation and vanishes for large time lag. This gives rise to a temporal noise model consisting of two components: alpha activity and additional random noise. The alpha activity is modeled as randomly occurring waves with random phase and the covariance of the additional noise decreases exponentially with lag. This model requires only six parameters instead of 12J(J 1). Theoretically, this model is stationary but in practice the stationarity of the matrix is highly influenced by the baseline correction. It appears that very good agreement between the data and the parametric model can be obtained when the baseline correction window is taken into account properly. This finding implies that the background noise is in principle a stationary process and that nonstationarities are mainly caused by the nature of the preprocessing method. When analyzing events at a fixed sample
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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Cited by 7 (4 self)
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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
The Wald Test and Cramér–Rao Bound for Misspecified Models in Electromagnetic Source Analysis
"... Abstract—By using signal processing techniques, an estimate of activity in the brain from the electro or magnetoencephalogram (EEG or MEG) can be obtained. For a proper analysis, a test is required to indicate whether the model for brain activity fits. A problem in using such tests is that often, ..."
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Abstract—By using signal processing techniques, an estimate of activity in the brain from the electro or magnetoencephalogram (EEG or MEG) can be obtained. For a proper analysis, a test is required to indicate whether the model for brain activity fits. A problem in using such tests is that often, not all assumptions are satisfied, like the assumption of the number of shells in an EEG. In such a case, a test on the number of sources (model order) might still be of interest. A detailed analysis is presented of the Wald test for these cases. One of the advantages of the Wald test is that it can be used when not all assumptions are satisfied. Two different, previously suggested, Wald tests in electromagnetic source analysis (EMSA) are examined: a test on source amplitudes and a test on the closeness of source pairs. The Wald test is analytically studied in terms of alternative hypotheses that are close to the null hypothesis (local alternatives). It is shown that the Wald test is asymptotically unbiased, that it has the correct level and power, which makes it appropriate to use in EMSA. An accurate estimate of the Cramér–Rao bound (CRB) is required for the use of the Wald test when not all assumptions are satisfied. The sandwich CRB is used for this purpose. It is defined for nonseparable least squares with constraints required for the Wald test on amplitudes. Simulations with EEG show that when the sensor positions are incorrect, or the number of shells is incorrect, or the conductivity parameter is incorrect, then the CRB and Wald test are still good, with a moderate number of trials. Additionally, the CRB and Wald test appear robust against an incorrect assumption on the noise covariance. A combination of incorrect sensor positions and noise covariance affects the possibility of detecting a source with small amplitude. Index Terms—Approximate model, constrained optimization, Fisher information with constraints, model checking, parameter covariance, separable least squares, source localization. I.
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.
2005b. Goodnessoffit and confidence intervals of approximate models
 Journal of Mathematical Psychology
"... 1 If the model for the data are strictly speaking incorrect, then how can one test whether the model fits? Standard goodnessoffit (GOF) tests rely on strictly correct or incorrect models. But in practice the correct model is not assumed to be available. It would still be of interest to determine ..."
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1 If the model for the data are strictly speaking incorrect, then how can one test whether the model fits? Standard goodnessoffit (GOF) tests rely on strictly correct or incorrect models. But in practice the correct model is not assumed to be available. It would still be of interest to determine how good or how bad the approximation is. But how can this be achieved? If it is determined that a model is a good approximation and hence a good explanation of the data, how can reliable confidence intervals be constructed? In this paper an attempt is made to answer the above questions. Several GOF tests and methods of constructing confidence intervals are evaluated both in a simulation and with real data from the internet based daily news memory test. 1
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"... discussions on this topic. The research of HMH is funded by an NWOVIDI grant. ..."
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discussions on this topic. The research of HMH is funded by an NWOVIDI grant.
Département d'Informatique FACULTÉ DES SCIENCES Professeur Thierry Pun Robust Focalized Brain Activity Reconstruction
"... présentée à la Faculté des Sciences de l'Université de Genève pour obtenir le grade de Docteur ès Sciences, mention Informatique par ..."
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présentée à la Faculté des Sciences de l'Université de Genève pour obtenir le grade de Docteur ès Sciences, mention Informatique par
SENSOR ARRAY SIGNAL PROCESSING AND THE NEUROELECTROMAGNETIC INVERSE PROBLEM IN FUNCTIONAL CONNECTIVITY ANALYSIS OF THE BRAIN
"... Sensor array signal processing and the neuroelectromagnetic inverse problem in functional connectivity analysis of the brain ..."
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Sensor array signal processing and the neuroelectromagnetic inverse problem in functional connectivity analysis of the brain