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Paper

  • C. Li, X. Huang, R. Song, R. Qian, X. Liu, X. Chen, “EEG-based seizure prediction via Transformer guided CNN.” Measurement, 2022, 203: 111948. Published.
  • X. Huang, C. Li, A. Liu, R. Qian, and X. Chen, “EEGDfus: A Conditional Diffusion Model for Fine-Grained EEG Denoising.” IEEE Journal of Biomedical and Health Informatics, Accepted for Publication, November 19, 2024.
  • X. Huang, C. Li, A. Liu, R. Qian, and X. Chen, “Unpaired EEG Denoising Via Contrastive Learning-Guided Generative Adversarial Network.” IEEE Transactions on Neural Networks and Learning Systems, Under Review, 2024.
  • X. Huang, C. Li, A. Liu, R. Qian, and X. Chen, “EEGKDNet: A Novel Bidirectional Denoising Framework for Real EEG Signals based on the Kolmogorov-Arnold Network.” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Under Review, 2023.


Degree Thesis

  • Research on the Removal of Artifacts in EEG Signals Using Deep Learning Methods.
    Xiaoyang Huang (Advisor: Xun Chen).
  • EEG-based seizure prediction via Transformer guided CNN.
    Xiaoyang Huang (Advisor: Chang Li, Xun Chen).
    China National University Student Innovation & Entrepreneurship Development Program.
    Proposal paper has been accepted by Measurement 2022