This simulation by Zeynep Akalin Acar at the Swartz Center for Computational Neuroscience (SCCN), UCSD, shows (on the left) 30 cm-sized cortical EEG source areas, animated by EEG source waveforms decomposed from actual EEG data using independent component analysis (ICA) in EEGLAB. The speed of the animation is one fifth real time; the color scale is (red +, blue -, green 0). The image on the right shows the summed projections of the 30 sources to the scalp -- i.e., the EEG field produced by these sources. The simulated EEG animation was computed in NFT using an accurate boundary element method (BEM) electrical head model based on a volunteer subject magnetic resonance (MR) head image. The animation illustrates the very broad point spread from small source areas of locally synchronous field activity (cortical EEG sources) to the scalp, and the character of the EEG data moving field pattern. The 'patchy' topographies of the scalp field reflect the summation of the spatially broad non-zero projections of a relatively few source areas in short intervals. These have no meaning in themselves; the concurrent non-zero values of the originating sources likely reflect random coincidences, though systematic study of event-related or spontaneous temporal relationships between multiple source values, for example using Tim Mullen's source information flow toolbox (SIFT) might reveal robust inter-source delay dependencies in some circumstances.