EEG-based emotion recognition using discriminative graph regularized extreme learning machine

@article{Zhu2014EEGbasedER,
  title={EEG-based emotion recognition using discriminative graph regularized extreme learning machine},
  author={Jia-Yi Zhu and Wei-Long Zheng and Yong Peng and Ruo-Nan Duan and Bao-Liang Lu},
  journal={2014 International Joint Conference on Neural Networks (IJCNN)},
  year={2014},
  pages={525-532}
}
This study aims at finding the relationship between EEG signals and human emotional states. Movie clips are used as stimuli to evoke positive, neutral and negative emotions of subjects. We introduce a new effective classifier named discriminative graph regularized extreme learning machine (GELM) for EEG-based emotion recognition. The average classification accuracy of GELM using differential entropy (DE) features on the whole five frequency bands is 80.25%, while the accuracy of SVM is 76.62… CONTINUE READING
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