Bridge for training deep neural-network models with neuroscientific data managed using MNE.

Focused on:

• Minimizing boilerplate for DNN powered BCI classifiers and processors
• Rapid integration and extension to new datasets by providing a yaml interface to dataset construction
• Platform for accessing state-of-the-art
  • Architectures (potentially with pretrained weights)
  • Pre-processing and data transformations

See guides and documentation at: https://dn3.readthedocs.io/en/latest/

Associated pre-print (article under review) can be found at: https://www.biorxiv.org/content/10.1101/2020.12.17.423197v1

Please consider citing the above in any scholarly work that uses this library.

• python >= 3.5
• pytorch >= 1.3
• mne >= 0.20
• pyyaml
• pyyaml-include
• numpy
• pandas
• tqdm