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 released: November 17, 2021

  •  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

  • Description:

    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.

  • MERL Contacts:
  • External Link:

    https://beetl.ai/introduction

  • Research Areas:

    Artificial Intelligence, Signal Processing, Human-Computer Interaction

    •  Han, M., Ozdenizci, O., Koike-Akino, T., Wang, Y., Erdogmus, D., "Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders", IEEE Journal of Biomedical and Health Informatics, DOI: 10.1109/​JBHI.2021.3062335, Vol. 25, No. 8, pp. 2928-2937, April 2021.
      BibTeX TR2021-027 PDF
      • @article{Han2021apr,
      • author = {Han, Mo and Ozdenizci, Ozan and Koike-Akino, Toshiaki and Wang, Ye and Erdogmus, Deniz},
      • title = {Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders},
      • journal = {IEEE Journal of Biomedical and Health Informatics},
      • year = 2021,
      • volume = 25,
      • number = 8,
      • pages = {2928--2937},
      • month = apr,
      • doi = {10.1109/JBHI.2021.3062335},
      • issn = {2168-2208},
      • url = {https://www.merl.com/publications/TR2021-027}
      • }
    •  Demir, A., Koike-Akino, T., Wang, Y., Erdogmus, D., "AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference", IEEE Access, DOI: 10.1109/​ACCESS.2021.3064530, Vol. 9, pp. 39955-39972, March 2021.
      BibTeX TR2021-016 PDF Presentation
      • @article{Demir2021mar,
      • author = {Demir, Andac and Koike-Akino, Toshiaki and Wang, Ye and Erdogmus, Deniz},
      • title = {AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference},
      • journal = {IEEE Access},
      • year = 2021,
      • volume = 9,
      • pages = {39955--39972},
      • month = mar,
      • doi = {10.1109/ACCESS.2021.3064530},
      • issn = {2169-3536},
      • url = {https://www.merl.com/publications/TR2021-016}
      • }
    •  Ozdenizci, O., Wang, Y., Koike-Akino, T., Erdogmus, D., "Learning Invariant Representations from EEG via Adversarial Inference", IEEE Access, DOI: 10.1109/​ACCESS.2020.2971600, Vol. 8, pp. 27074-27085, April 2020.
      BibTeX TR2020-049 PDF
      • @article{Ozdenizci2020apr,
      • author = {Ozdenizci, Ozan and Wang, Ye and Koike-Akino, Toshiaki and Erdogmus, Deniz},
      • title = {Learning Invariant Representations from EEG via Adversarial Inference},
      • journal = {IEEE Access},
      • year = 2020,
      • volume = 8,
      • pages = {27074--27085},
      • month = apr,
      • doi = {10.1109/ACCESS.2020.2971600},
      • issn = {2169-3536},
      • url = {https://www.merl.com/publications/TR2020-049}
      • }
    •  Ozdenizci, O., Wang, Y., Koike-Akino, T., Erdogmus, D., "Adversarial Deep Learning in EEG Biometrics", IEEE Signal Processing Letters, DOI: 10.1109/​LSP.2019.2906826, Vol. 26, No. 5, pp. 710-714, March 2019.
      BibTeX TR2019-017 PDF
      • @article{Ozdenizci2019mar2,
      • author = {{Ozdenizci, Ozan and Wang, Ye and Koike-Akino, Toshiaki and Erdogmus, Deniz}},
      • title = {Adversarial Deep Learning in EEG Biometrics},
      • journal = {IEEE Signal Processing Letters},
      • year = 2019,
      • volume = 26,
      • number = 5,
      • pages = {710--714},
      • month = mar,
      • doi = {10.1109/LSP.2019.2906826},
      • url = {https://www.merl.com/publications/TR2019-017}
      • }