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@misc{KDforearEEG,
      title={A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG Data}, 
      author={Mithunjha Anandakumar and Jathurshan Pradeepkumar and Simon L. Kappel and Chamira U. S. Edussooriya and Anjula C. De Silva},
      year={2022},
      eprint={2211.02638},
      archivePrefix={arXiv},
      primaryClass={eess.SP}
}

This repository contains implementation of cross-modal knowledge distillation approach combining response based distillation to enhance ear-EEG based sleep staging using the scalp-EEG.

The algorithms were developed in Pytorch Environment : https://pytorch.org/

pip install torch==1.10.0+cu113 torchvision==0.11.1+cu113 torchaudio===0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html

Run below code to install all other dependencies

pip install -r requirements.txt

Initially models are trained in supervised manner on scalp-EEG recordings, this pretrained models are used as teacher models in knowledge distillation training.

python main.py --model USleep --data_path <data path> --train_data_list [0,1,2,3,5,6,7] --val_data_list [8] --signals ear-eeg --training_type supervised --is_neptune True --nep_project <neptune project name> --nep_api <neptune API>

Offline Knowledge Distillation

In offline knowledge distillation, the pretrained supervised model is used as teacher model and student model is trained to learn the distribution of teacher model as well as the true targets.

python main.py --data_path <data path> --model_path <model path>

Online Knowledge Distillation

In online knowledge distillation, both teacher and student models are trained simultaneously.

python main.py --data_path <data path> --KD_type online

Transfer Learning

python main.py --data_path <data path> --is_retrain True --training_type supervised --model_path <model path>

Evaluation code script can be used to evaluate the saved models.

python evaluation.py --data_path <data path> --model_path <model path> --n_epochs 20

[1] Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ, Igel C.U-Sleep: resilient high-frequency sleep staging. npj Digit. Med. 4, 72 (2021).https://github.com/perslev/U-Time/blob/master/utime/models/usleep.py

[2] Pradeepkumar, J., Anandakumar, M., Kugathasan, V., Suntharalingham, D., Kappel, S. L., De Silva, A. C., & Edussooriya, C. U. Towards Interpretable Sleep Stage Classification Using Cross-Modal Transformers. arXiv preprint arXiv:2208.06991 (2022). https://github.com/Jathurshan0330/Cross-Modal-Transformer