The decision to employ postprocessing on electroencephalographic (EEG) data, toward the removal of undesirable artifacts, is associated with concerns of inadvertently filtering brain process data of interest to the research question. The rich data provided by multichannel EEGs supports a variety of postprocessing approaches. Brain process characteristics are often already well-studied and so the approach often impractical terms involves applying a postprocessing technique, and determining if the aggregate signal representing the brain process of interest matches those previously reported in the literature. However, as increased interest in real-time approaches to characterizing brain processes dominates the applied neuroergonomic literature, it is worth considering the absolute merits of various postprocessing techniques. For example, in event related potential/evoked response potential (ERP) work analyzed after collection it is common to utilize independent component analysis (ICA), which relies upon this statistical independence of variance accounted for by artifacts and separates them from variance accounted for by brain activity. ICA techniques, in effect,“clean” the waveform for analysis, preserving epics of interest. This is, however, a relatively computationally “expensive” approach for real-time applications. A relatively simple technique, moving window peak-to-peak amplitude detection (P2PW), uses differences between the highest and lowest voltages within successive epics of time to flag artifacts for removal. P2PW, therefore, does not preserve epics of interest, instead removes them entirely. The present work compares the performance of these two approaches in data collected by Sawyer et al. during an experiment which, for the first time, demonstrated the detection of the error related negativity (ERN) ERP in visual search for complex stimuli. In this work, participants completed tasks during 8 channel EEG recording, which was then analysed using ICA post-processing.