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In the graphical modelling of brain data, we are interested in estimating connectivity between various regions of interest, and evaluating statistical significance in order to derive a network model. This process involves aggregating results across frequency ranges and several patients, in order to obtain an overall result that can serve to construct a graph.In this paper, we propose a method based...
In the analysis of Electroencephalograms (EEG), notably in their graphical modeling, the estimation of the spectral matrix and associated variables is of central importance. Often, when adjusting for the bandwidth of the spectral matrix estimate, singularity issues arise and information derived from the inverse spectral matrix is intractable. This requires the use of regularization methods, which...
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