TR2022-121

quEEGNet: Quantum AI for Biosignal Processing


    •  Koike-Akino, T., Wang, Y., "quEEGNet: Quantum AI for Biosignal Processing", IEEE Conference on Biomedical and Health Informatics (BHI), September 2022.
      BibTeX TR2022-121 PDF Video Presentation
      • @inproceedings{Koike-Akino2022sep,
      • author = {Koike-Akino, Toshiaki and Wang, Ye},
      • title = {quEEGNet: Quantum AI for Biosignal Processing},
      • booktitle = {IEEE Conference on Biomedical and Health Informatics (BHI)},
      • year = 2022,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2022-121}
      • }
  • MERL Contacts:
  • Research Areas:

    Artificial Intelligence, Machine Learning, Signal Processing

Abstract:

In this paper, we introduce an emerging quantum machine learning (QML) framework to assist classical deep learning methods for biosignal processing applications. Specif- ically, we propose a hybrid quantum-classical neural network model that integrates a variational quantum circuit (VQC) into a deep neural network (DNN) for electroencephalogram (EEG), electromyogram (EMG), and electrocorticogram (ECoG) analysis. We demonstrate that the proposed quantum neural network (QNN) achieves state-of-the-art performance while the number of trainable parameters is kept small for VQC.

 

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  • Related Publication

  •  Koike-Akino, T., Wang, Y., "quEEGNet: Quantum AI for Biosignal Processing", arXiv, September 2022.
    BibTeX arXiv
    • @article{Koike-Akino2022sep2,
    • author = {Koike-Akino, Toshiaki and Wang, Ye},
    • title = {quEEGNet: Quantum AI for Biosignal Processing},
    • journal = {arXiv},
    • year = 2022,
    • month = sep,
    • url = {https://arxiv.org/abs/2210.00864}
    • }