SSVEP-DAN

This repository is the official implementation of "SSVEP-DAN: Data Alignment Network for SSVEP-based Brain Computer Interfaces".

Requirements

Step 1:

To install requirements:

git clone https://github.com/CECNL/SSVEP-DAN.git
cd SSVEP-DAN
conda env create -f SSVEP_DAN_env.yaml
conda activate SSVEP_DAN

Step 2:

Download Benchmark dataset and put them to the folder "Benchmark".

Download Wearable SSVEP BCI dataset and put them to the folder "Wearable".

Performance comparison for different calibration trials per stimulus

To obtain SSVEP-DAN performance on the 'Benchmark' scenarios, run this command:

python3 DANet_benchmark.py --gpu 0 --tps 2 --method DANet --file_path Testing/Benchmark_diff_ntps/2tps/ --model_path Testing/Benchmark_diff_ntps/2tps/

To obtain SSVEP-DAN performance on the 'Dry to dry' scenarios, run this command:

python3 DANet_wearable.py --gpu 0 --tps 2 --device dryTOdry --method DANet --file_path Testing/Wearable_diff_npts/dryTOdry/ --model_path Testing/Wearable_diff_npts/dryTOdry/

Performance comparison for different supplementary subjects

To obtain SSVEP-DAN performance on the 'Benchmark' scenarios, run this command:

python3 DANet_benchmark.py --gpu 0 --supp 5 --tps 2 --method DANet --file_path Testing/Benchmark_diff_supp/5supp/ --model_path Testing/Benchmark_diff_supp/5supp/

To obtain SSVEP-DAN performance on the 'Dry to dry' scenarios, run this command:

python3 DANet_wearable.py --gpu 0 --supp 5 --tps 2 --device dryTOdry --method DANet --file_path Testing/Wearable_diff_supp/dryTOdry/5supp --model_path Testing/Wearable_diff_supp/dryTOdry/5supp

Ablation study

To obtain SSVEP-DAN w/o pre-training performance on the 'Benchmark' scenarios, run this command:

python3 DANet_benchmark.py --gpu 0 --tps 2 --ablation wo1 --file_path Testing/Ablation/Benchmark/ --model_path Testing/Ablation/Benchmark/

To obtain SSVEP-DAN w/o fine-tuning performance on the 'Benchmark' scenarios, run this command:

python3 DANet_wearable.py --gpu 0 --tps 2 --device dryTOdry --ablation wo1 --file_path Testing/Ablation/dryTOdry/ --model_path Testing/Ablation/dryTOdry/

Reference

If you use this our codes in your research, please cite our paper and the related references in your publication as:

@article{,
  title={},
  author={},
  journal={arXiv preprint},
  year={2022}
}

If you use the TRCA, please cite the following:

@article{nakanishi2017enhancing,
  title={Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis},
  author={Nakanishi, Masaki and Wang, Yijun and Chen, Xiaogang and Wang, Yu-Te and Gao, Xiaorong and Jung, Tzyy-Ping},
  journal={IEEE Transactions on Biomedical Engineering},
  volume={65},
  number={1},
  pages={104--112},
  year={2017},
  publisher={IEEE}
}

If you use the LST, please cite the following:

@article{chiang2021boosting,
  title={Boosting template-based SSVEP decoding by cross-domain transfer learning},
  author={Chiang, Kuan-Jung and Wei, Chun-Shu and Nakanishi, Masaki and Jung, Tzyy-Ping},
  journal={Journal of Neural Engineering},
  volume={18},
  number={1},
  pages={016002},
  year={2021},
  publisher={IOP Publishing}
}