We aimed to investigate EEG signatures specifically associated with epileptic patterns of dynamic functional connectivity (dFC) found in BOLD-fMRI data. We estimated dFC using a sliding-window correlation analysis and applied dictionary learning (DL) to identify the most prominent patterns while forcing a certain degree of sparsity in time. Upon the labelling of each time window based on the pattern exhibiting the highest contribution, we investigated pattern-specific microstates (MS) and spectral proprieties in simultaneously recorded EEG data. In contrast with the spectral proprieties, EEG MS revealed robust signatures of epileptic dFC patterns in all patients.
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