This is the implementation for Decoupling Representation Learning for Imbalanced Electroencephalography Classification in Rapid Serial Visual Presentation Task in Journal of Neural Engineering.
This project is released under the Apache 2.0 license.
python >= 3.6
torch >= 1.7.0
numpy >= 1.20
tqdm >= 4.59.0
scipy >= 1.6.2
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modified the config file
{ "dataset": { "name": "Public", "subject_num": 1, "pairNum": 20000, "channel": 64 }, "train_para": { "batchsize_stage_1" : 1024, "batchsize_stage_2" : 128, "epoch_stage_1" : 60, "epoch_stage_2" : 20 }, "model_para": { "F1": 8, "F2": 2, "D": 1, "kernel_size": 3, "droup_out": 0.6 } }
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Train model
python main.py
If you use this toolbox or benchmark in your research, please cite this project.
@article{Li_2022,
doi = {10.1088/1741-2552/ac6a7d},
url = {https://doi.org/10.1088/1741-2552/ac6a7d},
year = 2022,
month = {may},
publisher = {{IOP} Publishing},
volume = {19},
number = {3},
pages = {036011},
author = {Fu Li and Hongxin Li and Yang Li and Hao Wu and Boxun Fu and Youshuo Ji and Chong Wang and Guangming Shi},
title = {Decoupling representation learning for imbalanced electroencephalography classification in rapid serial visual presentation task},
journal = {Journal of Neural Engineering}}