How much baseline correction do we need in ERP research? Extended GLM model can replace baseline correction while lifting its limits

TL;DR

This paper proposes an extended GLM model that replaces traditional baseline correction in ERP research, allowing data-driven determination of baseline correction needs and potentially improving statistical power.

Contribution

The study introduces a GLM-based approach that eliminates the need for fixed baseline correction, addressing its limitations and providing a flexible, data-driven alternative.

Findings

Extended GLM model can replace traditional baseline correction.

Including baseline as a predictor improves statistical power.

The method encompasses both full and no baseline correction as special cases.

Abstract

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.