There have been numerous applications of time-frequency approaches to ERP data which have added considerable support to the idea that theta and delta are separable processes underlying ERPs (Demiralp et al., 2001a; Demiralp et al., 2001b; Başar, et al., 1999; Başar et al., 2000), as well as to the characterization of midline frontal theta activity associated with a number of different tasks (Cavanagh, et al., 2011; Cohen et al., 2008; Cohen et al., 2007; Cavanagh et al., 2009b). The current report utilizes a recent TFPCA analysis approach based on the reduced interference distribution from Cohen's class of time-frequency transforms (Bernat et al., 2005). This approach has proven effective in parsing out frequency-independent components that occur in similar time windows from several common components such as the ERN (e.g. Bernat et al., 2005; Hall et al., 2007), the FN (Bernat et al., 2011; Nelson et al., 2011; Bernat et al., 2012), and the P3 (Bernat et al., 2011; Bernat et al., 2007; Gilmore et al., 2010). Thus, because this TF-PCA approach has been effective for assessing delta and theta for several
