r/clinicalEEG

I’ve been working with EEG (and MEG) for a while, and something I keep noticing is that many people can run analysis pipelines, but still feel unsure about what’s actually happening at each step. Things like filtering, referencing, epoching, ICA… they’re often applied, but not always fully understood. I’m curious how others here see it. What was the most confusing or difficult part for you when learning EEG analysis? Was it more the theory, the math, or the practical side of working with real data?

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?