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