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A fully automated method for segmentation of neonatal skull in Magnetic Resonance (MR) images for source localization of electrical/magnetic encephalography (EEG/MEG) signals is proposed. Finding the source of these signals shows the origin of an abnormality. We propose a hybrid algorithm in which a Bayesian classifying framework is combined with a Hopfield Neural Network (HNN) for neonatal skull...
We have developed an fMRI-MEG integrative neuroimaging method that is capable of analyzing spatiotemporal multiple cortical activities. The method determines the center of gravity in each fMRI activated cluster as the location of the linear constraints. First, activated multiple clusters were determined by statistically analyzing fMRI data. Secondly, using the fMRI activated clusters as spatial constraints,...
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