ADAPTIVE SEGMENTATION OF SPONTANEOUS EEG MAP SERIES INTO SPATIALLY DEFINED MICROSTATES

Space-oriented segmentation can decompose multi-channel EEG map series into time segments characterized by quasi-stationary field map config urations. This assesses the dynamics of the underlying processes as ac tivities of different neural generator ensembles. Our method of space- oriented segmentation describes the scalp field at times of maximal fi eld strength (Global Field Power) by the locations of the centroids of positive and negative map areas. A quantitative measure of the simult aneous distance of the centroid locations evaluates the similarity bet ween consecutive maps. A segment is defined as a sequence of maps that do not differ from each other by more than a preset value. Finally, t he average centroid locations for each segment are entered into an agg lomerative clustering procedure to obtain a set of distinct classes of field configurations. Four records of 16 s of 42-channel resting EEG (band-pass filtered 2-16 Hz) from six subjects were analyzed. Average segment duration was 157.9 ms. Most segments belonged to a small numbe r of classes (from 2 to 6, mean 3.7 classes for 90% of analysis time). The most frequent class showed an anterior-posterior field orientatio n and covered from 45 to 74% (mean 55% across subjects) of total time, with an average duration of 265 ms. The procedure was also tested usi ng temporally and spatially unstructured data (white noise and randoml y shuffled EEG) to ascertain that the methods reflect the spatio-tempo ral structure of the EEG processes.