DeepConvNet Usage
View source on GitHubTable of contents
About DeepConvNet
If you use the DeepConvNet model in your research, please cite the following paper:
@article{hbm23730,
author = {Schirrmeister Robin Tibor and
Springenberg Jost Tobias and
Fiederer Lukas Dominique Josef and
Glasstetter Martin and
Eggensperger Katharina and
Tangermann Michael and
Hutter Frank and
Burgard Wolfram and
Ball Tonio},
title = {Deep learning with convolutional neural networks for EEG decoding and visualization},
journal = {Human Brain Mapping},
volume = {38},
number = {11},
pages = {5391-5420},
keywords = {electroencephalography, EEG analysis, machine learning, end‐to‐end learning, brain–machine interface, brain–computer interface, model interpretability, brain mapping},
doi = {10.1002/hbm.23730},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.23730}
}
Preparation for DeepConvNet’s input
An example of DeepConvNet’s input on the BCIC IV 2a dataset (All settings are set up as optimal settings in the original paper).
Downloading a particular dataset
python download_datasets.py --dataset 'BCIC2a'
Preprocessing based on time domain EEG over the considered dataset
python prep_time_domain.py --dataset 'BCIC2a'
Build, fit, and evaluate DeepConvNet
# Subject-dependent MI classification
python run_DeepConvNet.py --model_name 'DeepConvNet' --dataset 'BCIC2a' --train_type 'subject_dependent' --data_type 'time_domain' --num_class 2 --num_chs 20 --GPU 0
# Subject-independent MI classification
python run_DeepConvNet.py --model_name 'DeepConvNet' --dataset 'BCIC2a' --train_type 'subject_independent' --data_type 'time_domain' --num_class 2 --num_chs 20 --GPU 0
Arguments:
| Arguments | Description | Default | |
|---|---|---|---|
| model_name | str prefix to save model | ‘DeepConvNet’ | |
| dataset | str prefix to pick up a particular dataset and save model | ‘BCIC2a’ | |
| train_type | str prefix to pick up a particular traning manner and save model | ‘subject_dependent’ | |
| data_type | str prefix to select data type of input | ‘time_domain’ | |
| num_class | int number of classes | 2 | |
| num_chs | int number of classes | 20 | |
| log_dir | str path to save model | ‘logs’ | |
| subjects | int or list of int or None list of range test subject, if None, use all subjects | None | |
| GPU | str select GPU ID | 0 | - |