• Corpus ID: 233481286

Distilling EEG Representations via Capsules for Affective Computing

@article{Zhang2021DistillingER,
  title={Distilling EEG Representations via Capsules for Affective Computing},
  author={Guangyi Zhang and Ali Etemad},
  journal={ArXiv},
  year={2021},
  volume={abs/2105.00104}
}
Affective computing with Electroencephalogram (EEG) is a challenging task that requires cumbersome models to effectively learn the information contained in large-scale EEG signals, causing difficulties for real-time smart-device deployment. In this paper, we propose a novel knowledge distillation pipeline to distill EEG representations via capsule-based architectures for both classification and regression tasks. Our goal is to distill information from a heavy model to a lightweight model for… 

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