Yujun Yan(yujun.yan@dartmouth.edu), Wenzhan Song(wsong@uga.edu), Xiang Zhang(xiang.zhang@charlotte.edu)
The recommended requirements are specified as follows:
- Python==3.10
- einops==0.4.0
- matplotlib==3.7.0
- numpy==1.23.5
- pandas==1.5.3
- patool==1.12
- reformer-pytorch==1.4.4
- scikit-learn==1.2.2
- scipy==1.10.1
- sympy==1.11.1
- torch==2.5.1+cu121
- tqdm==4.64.1
- natsort~=8.4.0
- mne==1.9.0
- mne-icalabel==0.7.0
- h5py==3.13.0
- pyedflib==0.1.40
- linear_attention_transformer==0.19.1
- timm~=0.6.13
- transformers~=4.57.1
The dependencies can be installed by:
pip install -r requirements.txtThe datasets used in this paper are all public datasets,
and the data processing scripts are provided in data_processing/ folder,
as well as readme files for each dataset.
Before running, make sure you have all the processed datasets put under dataset/.
Run meta_run.sh to reproduce all the experiments in paper.
The results can be found in results/method_name/,
and the checkpoints can be found in checkpoints/method_name/.