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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.
An Algorithm for Determination of the Number of Modes for
"... An algorithm for determination of the number of modes in a graylevel image histogram is presented in this paper. The hypothesis is that the image histogram's pdf is approached by a mixture of Gaussians. Then, the algorithm tries to estimate the number of components in the mixture, which is an impor ..."
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An algorithm for determination of the number of modes in a graylevel image histogram is presented in this paper. The hypothesis is that the image histogram's pdf is approached by a mixture of Gaussians. Then, the algorithm tries to estimate the number of components in the mixture, which is an important parameter when using the maximum likelihood technique to estimate the remaining of parameters of the mixture. The algorithm is divided into two parts. First, initial clustering using the kmeans algorithm is performed. This allows to estimate the centers of each cluster. Second, a novel algorithm, denoted "Elimination of False Clusters" (EFC) based on the Gaussian characteristics tries to suppress clusters which have no corresponding modes in the histogram. The algorithm has been validated on both artificial and real histograms. Keywords: pdf estimation, mixture models, unsupervised learning, graylevel image histogram 1 INTRODUCTION Estimation of a histogram's probability density fu...
Effective connectivity of fMRI data using ancestral graph theory: Dealing with missing regions
"... Most of the current methods to assess effective connectivity from functional magnetic resonance imaging (fMRI) rely on the assumption that all relevant brain regions are entered into the analysis. If this assumption is untenable, which we believe is most often the case, then spurious connections bet ..."
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Most of the current methods to assess effective connectivity from functional magnetic resonance imaging (fMRI) rely on the assumption that all relevant brain regions are entered into the analysis. If this assumption is untenable, which we believe is most often the case, then spurious connections between brain regions can appear. In this paper we propose to use an ancestral graph to model connectivity, which provides a way to avoid spurious connections. The ancestral graph is determined from trialbytrial variation and not from the time series. A random effects model is defined for ancestral graphs which allows for individual differences. The framework of local misspecification in the random effects model is used, which allows for modeling errors in connections and brain regions. The framework of local misspecification additionally provides a test on parameters in the graph which is robust against model misspecification. The test can be used to find differences in connection strength between, for example, conditions. Monte Carlo simulations show that the ancestral graph is appropriate to use even with modeling errors. To assess the accuracy further, the proposed method was applied to real fMRI data to determine how brain regions interact during speech monitoring.
Performance evaluation of clustering techniques for image segmentation
"... In this paper, we tackle the performance evaluation of two clustering algorithms: EFC and AICbased. Both algorithms face the cluster validation problem, in which they need to estimate the number of components. While EFC algorithm is a direct method, the AICbased is a verificative one. For a fair q ..."
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In this paper, we tackle the performance evaluation of two clustering algorithms: EFC and AICbased. Both algorithms face the cluster validation problem, in which they need to estimate the number of components. While EFC algorithm is a direct method, the AICbased is a verificative one. For a fair quantitative evaluation, comparisons are conducted on numerical data and image histograms data are used. We also propose to use artificial data satisfying the overlapping rate between adjacent components. The artificial data is modeled as a mixture of univariate normal densities as they are able to approximate a wide class of continuous densities.