Download the BCIC IV-2A and IV-2B dataset from here.
Download the ZuCo-TSR dataset from here.
MNRED dataset will be released in the near future.
Each dataset corresponds to a dataloader and a preprocessing scripts.
For example, smr_preprocess() in data/smr.py process BCIC IV-2A to SMR128.npy
Use default scripts in scripts/ to train any implemented model in model/.
All default hyperparameters among these models are tuned for MNRED datasets.
Wandb is needed if visualization of training parameters is wanted
run script like this:
python main.py \
--model DFaST \
--num_repeat 5 \
--dataset MNRED \
--data_dir /data/MNRED/MNRED.npy \
--sparsity 0.6 \
--batch_size 16 \
--num_epochs 100 \
--frequency 200 \
--num_kernels 64 \
--window_size 30 \
--D 30 \
--p1 8 \
--p2 16 \
--drop_last True \
--num_heads 4 \
--learning_rate 1e-4 \
--dropout 0.1 \
--schedule cos \
--do_train \
--do_evaluateFor other baseline models, more hyperparameter can be specified in config.py
and their own ModelConfig in corresponding model files
- python==3.10
- braindecode==0.4.85
- einops
- mne
- nilearn==0.9.2
- ptwt==0.1.7
- scikit-learn==1.2.1
- scipy
- torch==2.1.0
- wandb
If you are interested to leave a message, please feel free to send any email to us at chenweiguo@nudt.edu.cn