Abstract
Traditional EEG and EP analysis is trace-oriented. When mapping became popular, results of waveform analysis were mapped. Increased exposure to brain field maps has begun to orient analysis to the spatial aspects. Different maps must be generated by different neuronal populations; this offers direct key to the analysis of higher brain function. Space-oriented data reduction selects maps with optimal signal/noise ratio using Global Dissimilarity index. Classification and statistics of map landscapes uses extracted descriptors (locations of extrema or centroids) or three-dimensional dipole models. Map classification leads to adaptive segmentation of evoked or spontaneous map series into functional micro-states, the putative building blocks of perception and cognition.
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