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.

“An image says more than a thousand words” is a platitude, but when it comes to communicating the results of your research it is definitely true. Figures are probably the most important part of a paper and most readers will first look at them before reading the text in detail. In this blog post I’ll show how to use the Python library Matplotlib for creating publication-ready figures. For this purpose I’ll reproduce a figure from a recent paper step-by-step.