Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders

@article{Han2021UniversalPR,
  title={Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders},
  author={Mo Han and Ozan {\"O}zdenizci and T. Koike-Akino and Ye Wang and Deniz Erdoğmuş},
  journal={IEEE journal of biomedical and health informatics},
  year={2021},
  volume={PP}
}
Human computer interaction (HCI) involves a multidisciplinary fusion of technologies, through which the control of external devices could be achieved by monitoring physiological status of users. However, physiological biosignals often vary across users and recording sessions due to unstable physical/mental conditions and task-irrelevant activities. To deal with this challenge, we propose a method of adversarial feature encoding with the concept of a Rateless Autoencoder (RAE), in order to… Expand

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