Adapted from https://github.com/sylvchev/moabb_minischool

SSVEP Workshop

Interactive SSVEP signal processing and classification workshop using MOABB and MNE.

Goals:

• Learn to work with EEG data in python using MOABB and MNE.
• Implement an EEG preprocessing pipeline.
• Get acquainted with state-of-the-art SSVEP classification algorithms.

Installation

For this workshop, you'll need a working python environment (version 3.9). Preferably, make sure you have a python environment manager like Anaconda or virtualenv installed and pip to fetch the necessary packages.

Using conda

conda create --name ssvep_workshop --file environment.yml

Using virtualenv+pip

virtualenv .venv
source .venv/bin/activate
pip install -r requirements.txt

Running the interactive notebooks

After installing the requirements, execute the following:

jupyter notebook

and open the interactive notebooks in your favorite web browser.

Useful resources

• MNE documentation: https://mne.tools/stable/index.html
• MOABB documentation: https://neurotechx.github.io/moabb/index.html
• pyRiemann documentation: https://pyriemann.readthedocs.io/en/latest/index.html
• MOABB minischool by Sylvain Chevalier: https://github.com/sylvchev/moabb_minischool
• Mike X Cohen's Analyzing Neural Time Series videos: https://www.youtube.com/channel/UCUR_LsXk7IYyueSnXcNextQ/playlists?view=50&sort=dd&shelf_id=1