An adversarial approach to guide the selection of preprocessing pipelines for ERP studies
Electroencephalography (EEG) data are inherently contaminated by non-neuronal noise, including eye movements, muscle activity, cardiac signals, electrical interference, and technical issues such as poorly connected electrodes. Preprocessing to remove these artefacts is essential, yet the optimal method remains unclear due to the vast number of available techniques, their combinatorial use in pipelines, and adjustable parameters. Consequently, most studies adopt ad hoc preprocessing strategies based on dataset characteristics, study goals, and researcher expertise, with little justification for their choices. Such variability can influence downstream results, potentially determining whether effects are detected, and introduces risks of questionable analytical practices. Here, we present a method to objectively evaluate and compare preprocessing pipelines. Our approach uses realistically simulated signals injected into real EEG data as "ground truth", enabling the assessment of a pipelines ability to remove noise without distorting neuronal signals. This evaluation is independent of the studys main analyses, ensuring that pipeline selection does not bias results. By applying this procedure, researchers can select preprocessing strategies that maximize signal-to-noise ratio while maintaining the integrity of the neural signal, improving both reproducibility and interpretability of EEG studies. Although the data presented here focuses on processing and analysis most relevant for ERP research, the method can be flexibly expanded to other types of analyses or signals.