TR2019-017

Adversarial Deep Learning in EEG Biometrics


    •  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}
      • }
  • MERL Contacts:
  • Research Areas:

    Artificial Intelligence, Signal Processing

Abstract:

Deep learning methods for person identification based on electroencephalographic (EEG) brain activity encounters the problem of exploiting the temporally correlated structures or recording session specific variability within EEG. Furthermore, recent methods have mostly trained and evaluated based on single session EEG data. We address this problem from an invariant representation learning perspective. We propose an adversarial inference approach to extend such deep learning models to learn session-invariant person-discriminative representations that can provide robustness in terms of longitudinal usability. Using adversarial learning within a deep convolutional network, we empirically assess and show improvements with our approach based on longitudinally collected EEG data for person identification from half-second EEG epochs.

 

  • 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
      Brief
      • 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.
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  • Related Research Highlights

  • Related Publication

  •  Ozdenizci, O., Wang, Y., Koike-Akino, T., Erdogmus, D., "Adversarial Deep Learning in EEG Biometrics", arXiv, March 2019.
    BibTeX arXiv
    • @article{Ozdenizci2019mar3,
    • author = {Ozdenizci, Ozan and Wang, Ye and Koike-Akino, Toshiaki and Erdogmus, Deniz},
    • title = {Adversarial Deep Learning in EEG Biometrics},
    • journal = {arXiv},
    • year = 2019,
    • month = mar,
    • url = {https://arxiv.org/abs/1903.11673}
    • }