The time-frequency PCA (TF-PCA) method has been detailed in previous reports (E. M. Bernat et al., 2007; E.M. Bernat et al., 2005; Gilmore et al., in press). Here, the primary features are outlined. Averaged target ERP data from each of the three electrodes, for each participant, were subjected to TF decomposition using the Cohen’s class reduced interference distribution (RID) transform. This TF transform resulted in averaged TF surfaces, which are representations of the overall energy of the event-related activity, within a frequency range of 0 – 7.5 Hz (through the upper limit of theta) and a time range from stimulus onset (0 ms) to 1000 ms post-stimulus. PCA, using the covariance matrix and varimax rotation, was then performed on these average TF surfaces to decompose the surfaces into separate TF-PCA components. The resulting TF surfaces represented each TF-PCA component’s matrix of rotated component loadings for each TF point. Five components were retained, based upon inspection of the scree plot of singular values, representing the relative variance accounted for by each component, for a break, or elbow. In a final step,
