From a signal processing perspective, TF approaches can offer a complete representation of activity in averaged time-domain signals typically used to measure P3, and thus have the potential to supplant current time-domain P3 measures and advance the utility of EEG/ERP data in this area. However, several obstacles have hampered adoption of TF approaches as a general replacement for standard time domain approaches to measuring P3. First, most TF methods do not directly target condition average waveforms, the level of analysis used in the vast majority of EEG/ERP studies of P3. Instead, applications have been more focused on information not available in condition averages, to make inferences about oscillatory dynamics in trial-level data or high-frequency activity (e.g. gamma activity, 30–50 Hz). Another obstacle has been the wavelet TF transform, which is the most widely used, but which lacks important properties such 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
