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.

mTRFpy is a re-implementation and expansion of the Matlab mTRF-toolbox in Python. It provides methods for estimating multivariate linear mappings between continuous time-series that are often used in neuroscience.

This Python package estimates the headpose from individual images. It was written to track the head position of participants during experiments in a sound-attenuated chamber.

We established an experimental setup to study sound localization behavior in a controlled environment using arrays of loudspeakers, digital signal processors, infra-red cameras and custom Python code.