This is an official work in PyTorch reimplementation of Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalography.
Download the CHB-MIT Dataset from here and extract it into a new folder named data.
Install the following requirements:
- Pytorch and torchvision
- sklearn
- pandas
- seaborn
- tensorboard
git clone https://github.com/ironing/Task-oriented-SSL-EEG-AD.git
cd Task-oriented-SSL-EEG-AD
python pretreatment.py
The Script will process and split raw edf files.
python train.py --epochs 300 --learning_rate 0.0001 --inplane 18 --length 769
The Script will train a model save it in the AD_models Folder. The --inplane flag means the number of EEG channels and the --length flag means the length of EEG segment.
python train.py --eval
This will run five random seeds and report mean AUC, F1-score and EER.