The other models compared with AT-DGNN and unitized in the source code are listed as follows.
LGGNet: Learning from local-global-graph representations for brain–computer interface
@article{ding2023lggnet,
title={Lggnet: Learning from local-global-graph representations for brain--computer interface},
author={Ding, Yi and Robinson, Neethu and Tong, Chengxuan and Zeng, Qiuhao and Guan, Cuntai},
journal={IEEE Transactions on Neural Networks and Learning Systems},
year={2023},
publisher={IEEE}
}
EEGNet: A compact convolutional neural network for EEG-based brain–computer interfaces
@article{lawhern2018eegnet,
title={EEGNet: a compact convolutional neural network for EEG-based brain--computer interfaces},
author={Lawhern, Vernon J and Solon, Amelia J and Waytowich, Nicholas R and Gordon, Stephen M and Hung, Chou P and Lance, Brent J},
journal={Journal of neural engineering},
volume={15},
number={5},
pages={056013},
year={2018},
publisher={iOP Publishing}
}
Deep learning with convolutional neural networks for EEG decoding and visualization
@article{schirrmeister2017deep,
title={Deep learning with convolutional neural networks for EEG decoding and visualization},
author={Schirrmeister, Robin Tibor and Springenberg, Jost Tobias and Fiederer, Lukas Dominique Josef and Glasstetter, Martin and Eggensperger, Katharina and Tangermann, Michael and Hutter, Frank and Burgard, Wolfram and Ball, Tonio},
journal={Human brain mapping},
volume={38},
number={11},
pages={5391--5420},
year={2017},
publisher={Wiley Online Library}
}
Tsception: a deep learning framework for emotion detection using EEG
@inproceedings{ding2020tsception,
title={Tsception: a deep learning framework for emotion detection using EEG},
author={Ding, Yi and Robinson, Neethu and Zeng, Qiuhao and Chen, Duo and Wai, Aung Aung Phyo and Lee, Tih-Shih and Guan, Cuntai},
booktitle={2020 international joint conference on neural networks (IJCNN)},
pages={1--7},
year={2020},
organization={IEEE}
}
@inproceedings{ingolfsson2020eeg,
title={EEG-TCNet: An accurate temporal convolutional network for embedded motor-imagery brain--machine interfaces},
author={Ingolfsson, Thorir Mar and Hersche, Michael and Wang, Xiaying and Kobayashi, Nobuaki and Cavigelli, Lukas and Benini, Luca},
booktitle={2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
pages={2958--2965},
year={2020},
organization={IEEE}
}
@article{musallam2021electroencephalography,
title={Electroencephalography-based motor imagery classification using temporal convolutional network fusion},
author={Musallam, Yazeed K and AlFassam, Nasser I and Muhammad, Ghulam and Amin, Syed Umar and Alsulaiman, Mansour and Abdul, Wadood and Altaheri, Hamdi and Bencherif, Mohamed A and Algabri, Mohammed},
journal={Biomedical Signal Processing and Control},
volume={69},
pages={102826},
year={2021},
publisher={Elsevier}
}
Physics-informed attention temporal convolutional network for EEG-based motor imagery classification
@article{altaheri2022physics,
title={Physics-informed attention temporal convolutional network for EEG-based motor imagery classification},
author={Altaheri, Hamdi and Muhammad, Ghulam and Alsulaiman, Mansour},
journal={IEEE transactions on industrial informatics},
volume={19},
number={2},
pages={2249--2258},
year={2022},
publisher={IEEE}
}
EEG emotion recognition using dynamical graph convolutional neural networks
@article{song2018eeg,
title={EEG emotion recognition using dynamical graph convolutional neural networks},
author={Song, Tengfei and Zheng, Wenming and Song, Peng and Cui, Zhen},
journal={IEEE Transactions on Affective Computing},
volume={11},
number={3},
pages={532--541},
year={2018},
publisher={IEEE}
}