DATA-DETERMINED WINDOW SIZE AND SPACE-ORIENTED SEGMENTATION OF SPONTANEOUS EEG MAP SERIES

For the segmentation of series of momentary potential distribution map s into epochs of quasi-stable landscape (brain electric microstates), the maps are reduced to extracted landscape descriptors. Changes of th e descriptors over time are recognized as segment terminators. The sel ection of the descriptors' tolerated variance (the window size) determ ines the result. We present a window-determining function which allows a data-driven determination of the optimal window size, based on equa l weight given to the recognition of similarity and dissimilarity betw een maps. Segmentations based on two map descriptors (locations of ext reme potentials and centroids) were used on 211 two-second map epochs from 8 normal subjects for validation of the window-determining functi on and to establish normative data. Using the data-determined window s izes for segmentation, the mean duration of the obtained microstates a cross subjects did not differ between descriptors (144 and 143 msec, r espectively). Random permutation of the maps in time produced signific antly shorter segments, ensuring that the segmentation disclosed real properties of the original data and not artifacts of the procedure.