One thing I’ve been thinking about recently is how difficult it is to design preprocessing pipelines that generalize well across different EEG datasets and subjects.
Automated preprocessing workflows can definitely make analysis faster and more scalable, but in practice, data quality and artifacts can vary a lot between subjects. Sometimes decisions that work well for one recording may not be appropriate for another.
Things like filtering, bad channel rejection, ICA component selection, epoch rejection, and referencing strategies often still depend heavily on human judgment and the specific research question.
At the same time, I wonder whether more advanced AI models trained on large amounts of EEG data could eventually improve this and make preprocessing more adaptive and reliable.
Curious to hear how others see this.
Do you think EEG preprocessing can realistically become mostly automated, or will expert human supervision always remain essential?