A number of papers have demonstrated the usefulness of decomposing EEG data into ICs, not only for isolating artifactual contributions to scalp EEG, but also for studying on-going oscillatory activity, and event-related activity that contribute to ERP deflections recorded at the scalp (for examples see Debener et al., 2005; Onton et al., 2005; Eichele et al., 2010). Here, ICA is used to identify independent components that represent early (<200 ms) activity evoked by presentation of a simple visual stimulus, in order to compare single-trial EEG variability in those with and without ASD. In addition to decomposing the data with ICA, the EEG epochs were converted to current source density (CSD) models (e.g., Kayser and Tenke, 2006). CSD transforms compute the second spatial derivative of voltage between nearby electrode sites, which enhances local electrical activity while attenuating distal activity. By emphasizing local contributions to the surface map, some of the variability associated with spatial smearing via volume conduction may be reduced. Comparing measures of variability obtained from the raw channel EEG with measures of variability obtained from two different methods of
