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This notebook owes much of its content to the MNE and PyRiemann packages.
Most of the examples were partially inspired by the well-documented and high-quality tutorials available in the official documentations (most of which, by the way, are also available as Jupyter notebooks).
To all the developers who have contributed to these modules:
Running with Docker
Build the image
docker build --rm --no-cache eegbci_tutorial .
Run it
docker run -p 8888:8888 -e NB_UID=$(id -u) eegbci_tutorial
Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O'Reilly.
Nam, C. S., Nijholt, A., & Lotte, F. (Eds.). (2018). Brain-computer interfaces handbook: Technological and theoretical advances. Taylor & Francis, CRC Press.
Niedermeyer, E., Schomer, D. L., & Lopes da Silva, F. H. (Eds.). (2011). Niedermeyer’s electroencephalography: Basic principles, clinical applications, and related fields (6th edition). Wolters Kluwer, Lippincott Williams & Wilkins.
Packages
MNE - Open-source Python software for exploring, visualizing, and analyzing human neurophysiological data: MEG, EEG, sEEG, ECoG, and more
Numpy - Fundamental package for scientific computing with Python
PyRiemann - a python package for covariance matrices manipulation and classification through riemannian geometry
Sites
BNCI Horizon 2020 - The Future of Brain/Neural Computer Interaction: Horizon 2020
EDFbrowser - Free, opensource, multiplatform, universal viewer and toolbox intended for, but not limited to, timeseries storage files like EEG, EMG, ECG, BioImpedance, etc.