This is code for the project on deep domain adaptation for EEG motor imagery classification. We implement a pipeline which combines and extends *Parallel Spatial–Temporal Self-Attention CNN-Based Motor Imagery Classification for BCI by Liu et al. and *Domain-Adversarial Training of Neural Networks by Ganin et al.
- Pytorch 1.7
- Python 3.9
- MNE tools
We use BCI competition *IV Graz dataset 2a Before running the code, ensure that you created a directory
dataset/2a
where you have put all of the competition .gdf files. Also you need .mat files with the annotations.
Run python main.py, which represents the main training script. It has a number of parametes you may want to tinker with.
--dataroot
default='dataset/2a/'
Location of th Graz dataset
--batch
default=20
Batch size for training. Evaluation batch size will be twice as big.
--lr
default = 1e-5
Learning rate for the Adam optimizer
--epochs
default=200
Number of epochs to train for
--annotated
default=0.0
The default ratio of annotated examples from the target domain to learn from. Increase it to increase the ammount of annotated target domain data to train.
--features'
default='attention')
Pick a feature generator for DANN. "concat" just processes the signal, "attention" is equivalent to PSTSA, double_attention add additional attention layer to PSTSA'
--dropout'
Usage of dropout. Improves performance in some cases, original papers didn't use it.
--target_weight
default = 1.0
Increases lr for target domain on classification to make training on it more substantial.
main.py contains the main trainin and evaluation scripts. Evaluation will be performed automatically after training.
model.py contains all of the model modules that we use in this project
data_loader.py contains our dataloader for the gratz dataset.
https://github.com/Shenyonglong/Spatial-Temporal-attention- https://github.com/fungtion/DANN_py3