Bellow you can find some openly available material from our lab.
We have developped a series of tutorials for analyzing EEG data using signal processing and machine learning techniques, thanks to support from Mozilla Science Lab. We welcome contributions through the project’s repository.
Here you can find material for the PIP Summer school on Machine Learning, including a tutorial for applying maching learning techniques on EEG data.
You can find here the material with hands-on exercises for the workshop Algorithmic decision-making in neuroscience: how can we improve algorithmic interpretability and reduce bias?, that Florence Aellen and Athina Tzovara gave at the Applied Machine Learning Days 2022.
Scripts to create pure tones in python and to run an auditory oddball paradigm, using pygame.
Wrapper functions to import Neuralynx data in fieldtrip and transform them to .fif format, which can be then read in MNE, python. Code can be found here.