Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders


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 taskirrelevant 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 exploit disentangled, nuisance-robust, and universal representations. We achieve a good trade-off between user-specific and task-relevant features by making use of the stochastic disentanglement of the latent representations by adopting additional adversarial networks. The proposed model is applicable to a wider range of unknown users and tasks as well as different classifiers. Results on cross-subject transfer evaluations show the advantages of the proposed framework, with up to an 11.6% improvement in the average subject-transfer classification accuracy.


  • Related News & Events

    •  AWARD    MERL Ranked 1st Place in Cross-Subject Transfer Learning Task and 4th Place Overall at the NeurIPS2021 BEETL Competition for EEG Transfer Learning.
      Date: November 11, 2021
      Awarded to: Niklas Smedemark-Margulies, Toshiaki Koike-Akino, Ye Wang, Deniz Erdogmus
      MERL Contacts: Toshiaki Koike-Akino; Ye Wang
      Research Areas: Artificial Intelligence, Signal Processing, Human-Computer Interaction
      • The MERL Signal Processing group achieved first place in the cross-subject transfer learning task and fourth place overall in the NeurIPS 2021 BEETL AI Challenge for EEG Transfer Learning. The team included Niklas Smedemark-Margulies (intern from Northeastern University), Toshiaki Koike-Akino, Ye Wang, and Prof. Deniz Erdogmus (Northeastern University). The challenge addresses two types of transfer learning tasks for EEG Biosignals: a homogeneous transfer learning task for cross-subject domain adaptation; and a heterogeneous transfer learning task for cross-data domain adaptation. There were 110+ registered teams in this competition, MERL ranked 1st in the homogeneous transfer learning task, 7th place in the heterogeneous transfer learning task, and 4th place for the combined overall score. For the homogeneous transfer learning task, MERL developed a new pre-shot learning framework based on feature disentanglement techniques for robustness against inter-subject variation to enable calibration-free brain-computer interfaces (BCI). MERL is invited to present our pre-shot learning technique at the NeurIPS 2021 workshop.
  • Related Research Highlights

  • Related Publication

  •  Han, M., Ozdenizci, O., Koike-Akino, T., Wang, Y., Erdogmus, D., "Soft-Disentangled Adversarial Transfer Learning for Universal Physiological Feature Extraction", arXiv, September 2020.
    BibTeX arXiv
    • @article{Han2020sep2,
    • author = {Han, Mo and Ozdenizci, Ozan and Koike-Akino, Toshiaki and Wang, Ye and Erdogmus, Deniz},
    • title = {Soft-Disentangled Adversarial Transfer Learning for Universal Physiological Feature Extraction},
    • journal = {arXiv},
    • year = 2020,
    • month = sep,
    • url = {}
    • }