Baseline correction plays an important role in past and current methodological debates in ERP research (e.g. the Tanner v. Maess debate in Journal of Neuroscience Methods), serving as a potential alternative to strong highpass filtering. However, the very assumptions that underlie traditional baseline also undermine it, making it statistically unnecessary and even undesirable and reducing signal-to-noise ratio. Including the baseline interval as a predictor in a GLM-based statistical approach allows the data to determine how much baseline correction is needed, including both full traditional and no baseline correction as subcases, while reducing the amount of variance in the residual error term and thus potentially increasing statistical power.
@article{arxiv.1707.08152,
title = {How much baseline correction do we need in ERP research? Extended GLM model can replace baseline correction while lifting its limits},
author = {Phillip M. Alday},
journal= {arXiv preprint arXiv:1707.08152},
year = {2019}
}