This spatial mixing of EEG has shaped the way that EEG data are analyzed, most notably by leading to a dominance in single-trial averaging to calculate the event-related potential (ERP). The theory behind the ERP technique is that by calculating an average of several time-locked trials, event unrelated activity, being phase- and time-random with respect to the time-locking event, cancels to near zero amplitude, whereas the part of the EEG that is time-locked to the relevant event remains visible in the signal. Single-trial analysis is therefore rejected in favor of “average” event-related analyses. However, given the value of understanding variability across single-trials, just as the SD provides vital information regarding the distribution of values around a mean response time, a number of alternate methods have been put forward for facilitating single-trial analysis of EEG data. These include complex filtering (Salajegheh et al., 2004), maximum likelihood estimation (Jaskowski and Verleger, 1999), parametric modeling (von Spreckelsen and Bromm, 1988), multivariate matching pursuit algorithms (Sieluzycki et al., 2009), general linear model analyses (Pernet et al., 2011), and decomposing data using ICA (Jung et
