as uniform TF resolution and accurate representation of the energy in the signal. Particularly relevant is that wavelets do not generally provide good time support for activity at lower frequencies (e.g. below 3 Hz), where a majority of energy is located in the time region containing standard time-domain EEG/ERP components such as the P3. Finally, because TF transforms add a dimension to the signal representation (TF versus time or frequency alone), the complexity and amount of data is greatly increased, creating a need for new data reduction techniques. The approaches taken in the current report address these problems by utilizing more advanced TF representation algorithms (Cohen’s class reduced interference distribution (RID: Cohen, 1995) as opposed to wavelets), and implementing an effective TF data reduction technique based on the widely-understood statistical technique of principal components analysis (E.M. Bernat, Williams, & Gehring, 2005). For example, in recent work we presented evidence that the time-frequency principal components analysis (TF-PCA) approach can disentangle overlapping theta and delta processes, and produce better measures of the relevant processes in a feedback task typically indexed with time-domain feedback related-negativity (FRN) and P3 components (E.M. Bernat, Nelson, Holroyd, Gehring, & Patrick, 2008). In that report, the TF-PCA approach
