• A Pytorch implementation of our paper "PR-PL: A Novel Prototypical Representation Based Pairwise Learning Framework for Emotion Recognition Using EEG Signals."
  
• arxiv

• Python 3.7
• Pytorch 1.3.1
• NVIDIA CUDA 9.2
• Numpy 1.20.3
• Scikit-learn 0.23.2
• scipy 1.3.1

• Prepare dataset: SEED and SEED-IV

• PR-PL model definition file: model_PR_PL.py
• Pipeline of the PR-PL: implementation_PR_PL.py
• implementation of domain adversarial training: Adversarial.py

• After modify setting (path, etc), just run the main function in the implementation_PR_PL.py

• The implementation code of domain adversarial training is bulit on the dalib code base

If you find our work helps your research, please kindly consider citing our paper in your publications. @ARTICLE{10160130, author={Zhou, Rushuang and Zhang, Zhiguo and Fu, Hong and Zhang, Li and Li, Linling and Huang, Gan and Li, Fali and Yang, Xin and Dong, Yining and Zhang, Yuan-Ting and Liang, Zhen}, journal={IEEE Transactions on Affective Computing}, title={PR-PL: A Novel Prototypical Representation Based Pairwise Learning Framework for Emotion Recognition Using EEG Signals}, year={2023}, volume={}, number={}, pages={1-14}, doi={10.1109/TAFFC.2023.3288118}}