Time-frequency (TF) analysis is a technique that can be used to quantify the time-varying spectral properties of ERP signals. This approach allows separation of activity that has either a unique time-course or rate of oscillation (frequency). Principal components analysis (PCA) of time-frequency transforms of the ERPs (see Bernat, et al., 2005) was applied in order to disaggregate FRN and P300 components. To enhance separation of theta and delta activity relevant to the FRN and P300 (as suggested by previous work; Bernat et al., 2005, 2007; Gilmore et al., 2009; Hall et al., 2007), brain response activity in the window of −1000 to +2000 ms relative to feedback stimulus onset was filtered in two distinct ways before applying the TF-PCA: 1) using consecutive 3 and 9 Hz high- and low-pass 3rd order Butterworth filters (respectively), to isolate theta-band activity, and 2) using a 3 Hz lowpass 3rd order Butterworth filter, to isolate delta-band activity. These theta- and delta-filtered signals were then each transformed into time-frequency energy distributions (surfaces) using the binomial reduced interference distribution (RID) variant of Cohen’s class of time-frequency
