Configuring an experiment is a key step in quantitative research. In this blog post, we will explore type hinting and data validation with Pydantic to automatically validate the experimental configuration. This allows us to catch locgical inconsitencies before they affect the actual experiment and leads to software that is more robust.
The second part of this series demonstrate additional preprocessing steps. Specifically, it addresses the problem of eye artifacts which are omnipresent in EEG recordings. It also demonstartes a procedure for repairing and rejecting noise-contaminated channels and segments.
Preprocessing is an important and controversial topic in EEG research. Here, I discuss it’s necessity and present a minimal preprocessing pipeline that deals with the most common sources of noise while avoiding to distort the data. I demonstrate each step using publicly available data.
