Abstract
A companion paper introduced the rERP framework, which recasts traditional event-related potential (ERP) averaging as a special case of a more flexible regression-based approach to estimating ERP waveforms. Here, we build on this foundation, showing how rERP analysis can also handle the estimation of nonlinear effects (a generalization of both the well-known approach of dichotomizing continuous covariates, and also of the ERP image technique), and can disentangle overlapping ERPs to temporally adjacent stimuli. We then consider how the use of rERPs impacts on other parts of the EEG analysis pipeline, including baselining, filtering, significance testing, and artifact rejection, and provide practical recommendations. Free software implementing these techniques is available.