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Interactive segmentation of EEG synchrony data in time-frequency space by means of region-growing and Bayesian regularization.

In this paper we present a new methodology for the interactive visualization and segmentation of electroencephalographic (EEG) scalp synchrony data. Synchrony measurements are estimated for all electrode pairs and classified as higher, lower, or equal than the baseline average. The classified values are then displayed in the form of Time-Frequency-Topography (TFT) maps, which can be segmented using a seeded region growing algorithm and a Bayesian regularization technique. Finally, we present the synchronization maps that result from the analysis of real EEG data from a figure categorization experiment.