Xinliang Zhou

I am a third year (Final year) PhD candidate at Nanyang Technological University (NTU), Singapore, developing innovative time series data mining, machine learning, and deep learning methods. My research focuses on advancing interpretable and robust artificial intelligence algorithms for physiological time series analysis, with particular interest in Electroencephalography (EEG) based Brain Computer Interfaces. Through my PhD work, I aim to create generalizable frameworks for reliable and trustworthy AI systems that can enable real-world healthcare and neural engineering applications. Resume Here

I’m open to any related collaboration, so feel free to contact me.



Selected Publications

Co-first Authors, * Corresponding Authors

Conference Papers

  1. VBH-GNN: Variational Bayesian Heterogeneous Graph Neural Networks for Cross-subject Emotion Recognition [PDF]
    • Chenyu Liu , Xinliang Zhou , Zhengri Zhu, Liming Zhai, Ziyu Jia*, and Yang Liu
      The Twelfth International Conference on Learning Representations ( ICLR 2024 )
  2. VSGT: Variational Spatial and Gaussian Temporal Graph Models for EEG-based Emotion Recognition [PDF]
    • Chenyu Liu , Xinliang Zhou , Jiaping Xiao, Zhengri Zhu, Liming Zhai, Ziyu Jia*, and Yang Liu
      The 33rd International Joint Conference on Artificial Intelligence ( IJCAI 2024 )
  3. An EEG Channel Selection Framework for Driver Drowsiness Detection via Interpretability Guidance [PDF]
    • Xinliang Zhou, Dan Lin, Ziyu Jia, Jiaping Xiao, Chenyu Liu, Liming Zhai, and Yang Liu
      45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society ( EMBC 2023 )
  4. EEG-based Sleep Staging with Hybrid Attention [PDF]
    • Xinliang Zhou, Chenyu Liu, Jiaping Xiao, and Yang Liu*
      The 2023 IEEE Conference on Artificial Intelligence ( IEEE CAI 2023 )
  5. EENED: End-to-End Neural Epilepsy Detection based on Convolutional Transformer [PDF]
    • Chenyu Liu, Xinliang Zhou, and Yang Liu*
      The 2023 IEEE Conference on Artificial Intelligence ( IEEE CAI 2023 )
  6. Graph Neural Networks in EEG-based Emotion Recognition: A Survey [Preprint]
    • Chenyu Liu , Xinliang Zhou , Yihao Wu, Ruizhi Yang, Liming Zhai, Ziyu Jia, and Yang Liu*

Journal Papers

  1. Learning Robust Global-Local Representations from EEG for Neural Epilepsy Detection [PDF]
    • Xinliang Zhou, Chenyu Liu, Ruizhi Yang, Liangwei Zhang, Liming Zhai, Ziyu Jia*, and Yang Liu
      IEEE Transactions on Neural Artificial Intelligence (IEEE TAI 2024)
  2. Hybrid Spiking Neural Network for Sleep Electroencephalogram Signals [PDF]
    • Ziyu Jia, Junyu Ji, Xinliang Zhou*, and Yuhan Zhou
      Science China Information Sciences 65.4 ( 2022 ): 140403.
  3. Interpretable and Robust AI in EEG Systems: A Survey [Preprint]
    • Xinliang Zhou, Chenyu Liu, Liming Zhai, Ziyu Jia, Cuntai Guan, and Yang Liu*
  4. Learning Relational Probabilistic Graphs for EEG-based Emotion Recognition [Preprint]
    • Xinliang Zhou, Chenyu Liu, Jiaping Xiao, Liming Zhai, Ziyu Jia*, and Yang Liu
  5. Learning Local to Global Spatial-Temporal Representation for Motor Imagery Classification ( Preprint )
    • Shaozhe Liu, Leike An, Xinliang Zhou, Xiaoqun Ning, and Ziyu Jia*



Academic Services

Reviewer for Academic Journals

  • IEEE Transactions on Neural Systems and Rehabilitation Engineering (IEEE TNSRE)
  • IEEE Transactions on Computational Social Systems (IEEE TCSS)
  • Bentham Science Current Bioinformatics

Reviewer for Academic Conferences

  • ACM International Conference on Multimedia (ACM MM)
  • Annual Conference on Neural Information Processing Systems (NeurIPS)
  • International Conference on Learning Representations (ICLR)
  • IEEE International Conference on Data Mining (ICDM)