Brooks, J.L., Zoumpoulaki, A., & Bowman, H. (2017). Data-driven region-of-interest selection without inflating Type I error rate. Psychophysiology, 54(1), 100-113.
****Please cite the above paper if you use our method****
During ERP data analysis, it is often difficult to know, a priori, precisely where effects will occur on the scalp, in time, and in frequency (for oscillations). To overcome this, researchers often identify regions-of-interest (ROIs), but have been criticized for sometimes using biased, data-driven methods which inflate Type I error rates. Through simulations and analysis of human ERP data, we demonstrate a data-driven ROI-selection method using the aggregated-grand average (AGAT) wave which controls Type I error rate. Furthermore, it relaxes the precision necessary for a priori ROI specification. We demonstrate that the AGAT is orthogonal to the experimental contrast and, importantly, show that other common methods for computing orthogonal waveforms for ROI selection can inflate Type I error rate under some conditions. Finally, we show that the AGAT method has superior statistical power over common a priori ROI selection methods by up to 60%. Our results demonstrate a simple, unbiased and data-driven ROI selection method which is relevant for replicable and powerful ERP analysis in studies of sensory and cognitive ERP components.
****Before using the AGAT, please see our list of assumptions for usage in Table 4 of our 2017 paper****
Our simulation work on the AGAT is ongoing and we are currently working on various questions related to how AGAT can be used to safely, and with high power, localise ROIs in ERP and other data sets. We will post more information here as this work is completed. However, if you have a particular usage that you are considering, please feel free to get in touch with Joseph Brooks to ask about whether we have any results for that approach. If not, you are welcome to use our simulation code (see above) as a basis for simulating your design.