full epochs to provide sufficient data to resolve low frequency activity (Bernat et al., 2005). PCA was applied to the TF transforms of the theta and delta filtered signals separately. This TF-PCA approach (based on the covariance matrix with a Varimax rotation; Bernat et al., 2005) was applied to each TF representation with a 0 to 14 Hz frequency window and 0 to 750 ms or 0 to 1000 ms post-stimulus time window for high and low frequency signals, respectively, to identify the underlying components in the dataset that accounted for the greatest variance across all TF data points.
