EEG Data Augmentation for Emotion Recognition Using a Conditional Wasserstein GAN

@article{Luo2018EEGDA,
  title={EEG Data Augmentation for Emotion Recognition Using a Conditional Wasserstein GAN},
  author={Yun Luo},
  journal={2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
  year={2018},
  pages={2535-2538}
}
  • Yun Luo
  • Published 1 July 2018
  • Medicine, Computer Science
  • 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Due to the lack of electroencephalography (EEG) data, it is hard to build an emotion recognition model with high accuracy from EEG signals using machine learning approach. [...] Key Method A Wasserstein GAN with gradient penalty is adopted to generate realistic-like EEG data in differential entropy (DE) form.Expand
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