Authors: Neeraj Wagh, Jionghao Wei, Samarth Rawal, Brent Berry, Leland Barnard, Benjamin Brinkmann, Gregory Worrell, David Jones, Yogatheesan Varatharajah

Affiliation: University of Illinois at Urbana-Champaign, Mayo Clinic

• Paper: https://proceedings.mlr.press/v158/wagh21a.html
• ML4H Poster: https://drive.google.com/file/d/1-B7joEquzr4kqsfSGDqksUARWsxlXOyA/view?usp=sharing
• ML4H 5-minute Video: https://recorder-v3.slideslive.com/?share=56322&s=8950fb88-3f3f-4490-92ff-760b73e5a4b5
• ML4H Slides: https://drive.google.com/file/d/1uUma2e4GKIn8Zo7dEQRl_WkyVcBLzpaR/view?usp=sharing
• Final Models, Pre-computed Features, Training Metadata: Box
• Raw Data: MPI LEMON (no registration needed), TUH EEG Abnormal Corpus (needs registration)

• A dockerfile is provided with all Python dependencies.

• Models:
  • FULLSSL in code refers to "HS BSE AC" in the paper.
  • DBRTriplet refers to "BSE AC".
  • BTTriplet refers to "HS AC".
  • BTDBR refers to "HS BSE".
  • Triplet refers to "AC only".
  • DBR refers to "BSE only".
  • BT refers to "HS only".
  • SOTA refers to "ShallowNet".
• Tasks:
  • "EEG Grade" is the "condition" task with TUH dataset.
  • "Eye State" is the "condition" task with Lemon dataset.

• Input choices for tasks: "condition", "gender", "age".
• Input choices for dataset: "lemon", "tuh".
• Input choices for ablation models: "FULLSSL", "DBRTriplet", "BTTriplet", "BTDBR", "Triplet", "DBR", "BT".

1. Download resources folder, which contains pre-computed feature arrays(power spectral density, topographical maps data, and preprocessed timeseries), metadata from Box. Place it in the root directory of the project.
2. Enter evaluation folder.
3. To evaluate linear baseline, run the following command:

python linear_baseline_eval.py --dataset={dataset choice} --task={task choice}

4. To evaluate proposed ablation models, run the following command:

python ablation_models_eval.py --gpu_idx={gpu index} --dataset={dataset choice} --task={task choice} --mode={ablation model choice}

5. To evaluate SOTA model, run the following command:

python SOTA_eval.py --gpu_idx={gpu index} --dataset={dataset choice} --task={task choice} 

1. Download resources folder from Box. Place it in the root directory of the project.
2. Enter Fine-tune folder.
3. To fine-tune ablation models, run the following command:

python ablation_pipeline.py --gpu_idx={gpu index} --dataset={dataset choice} --task={task choice} --mode={ablation model choice}

4. To fine-tune SOTA model, run the following command:

python SOTA_pipeline.py --gpu_idx={gpu index} --dataset={dataset choice} --task={task choice}

1. Download resources folder from Box. Place it in the root directory of the project.
2. Enter supervised_learning folder.
3. To train the supervised learning models, run the following command:

python linear_baseline_train.py --dataset={dataset choice} --task={task choice}

• Issues regarding non-reproducibility of results or support with the codebase should be emailed to Neeraj and John.
• Neeraj: [email protected] / Website / Twitter / Google Scholar
• John: [email protected]
• Yoga: [email protected] / Website / Google Scholar

Wagh, N., Wei, J., Rawal, S., Berry, B., Barnard, L., Brinkmann, B., Worrell, G., Jones, D. & Varatharajah, Y.. (2021). Domain-guided Self-supervision of EEG Data Improves Downstream Classification Performance and Generalizability. Proceedings of Machine Learning for Health, in Proceedings of Machine Learning Research 158:130-142 Available from https://proceedings.mlr.press/v158/wagh21a.html.