I am an M.Phil. student in Computer Science at the City University of Hong Kong. Prior to joining CityU, I received my bachelor's degree at the Northeast Electric Power University in Jilin, China, supervised by Prof. Yimin Hou and Dr. Jinglei Lv.

I am currently an internship student at Samsung Research in Beijing. In my undergraduate study, I interned at the NLP team of Philips Research, Shanghai. In summer 2017, I attended a summer school at the University of California, Irvine in CA, USA.

My research interests mainly lie in Computer Vision.
Some of my paper survey and presentations can be found


Graph Neural Network (GNN)

  • Dynamic Graph Convolutional Neural Networks Survey
  • Graph Convolutional Neural Networks (Chebyshev Approximation)
  • Natural Language Processing (NLP)

  • Graph Matching
  • Sub-word BPE Algorithm for NMT
  • Concept Matching for Medical Terms
  • Computer Vision (CV)

  • Deep Learning Models Compression and Acceleration
  • 3D Human Pose Estimation and Human Body Reconstruction
  • YOLO Object Detection
  • Other Tutorials

  • Usage of Cloud Server and Setting-up
  • Python Environment Setting-up
  • TensorFlow for Deep Learning
  • Crypto currency Return and Price Prediction with Machine Learning
  • Big Data Parallel Processing by PySpark and Horovod Distributed Deep Learning

  • Email:
    Scholar  GitHub  



    1. Deep Feature Mining via Attention-based BiLSTM-GCN for Human Motor Imagery Recognition

      Yimin Hou, Shuyue Jia *, Xiangmin Lun, Shu Zhang, Tao Chen, Fang Wang, Jinglei Lv
      Codes Paper

      Frontiers in Bioengineering and Biotechnology
      , 2022.
      See More
        We introduced a novel approach that combined Attention-based BiLSTM with the Graph Convolutional Neural Network (Graph CNN / GCN).
        • Open Source EEG-DL, a Deep Learning (DL) library written by TensorFlow for EEG Tasks (Signals) Classification.
        • Attention-based BiLSTM was firstly used to extract features from raw EEG signals. The followed GCN model classified the features of four EEG Motor Imagery (MI) tasks, imagining left fist, right fist, both fists, and both feet.
        • 98.81% and 94.64% accuracies have been achieved for the individual subject and a group of 20 subjects.
        • Benchmark Dataset: EEG Motor Movement/Imagery Dataset.
        Project3.1 Project3.2

    2. A Novel Approach of Decoding EEG Four-Class Motor Imagery Tasks via Scout ESI and CNN
      Codes Paper

      Yimin Hou, Lu Zhou, Shuyue Jia, Xiangmin Lun
      Journal of Neural Engineering
      , 2020.
      See More
        We presented a novel approach that could potentially improve the current stroke rehabilitation strategies by implementing a deep learning approach for an Electroencephalogram (EEG) based on MI Brain-Computer Interface System.
        • Constructed 6 convolutional layers, 2 max-pooling layers, and 3 FC layers CNNs for four-class motor imagery classification through TensorFlow, with 50% dropout (spatial dropout after every Conv layer and regular dropout for FC layers) – 11.44% accuracy improvement, batch normalization (BN) – 10.15% improvement, and Short-cut Connection – 1.76% improvement to prevent overfitting, and achieved SOTA results: 94.50% accuracy on scout R5, 94.54% at subject level, and 96% for left fist prediction.
        • Took charge of DNNs design, including methods comparisons, such as MLPs, CNNs, RNNs, and LSTMs, classification results calculations, and programming. 10 and 14 subjects’ data were utilized (19,320 and 27,048 samples in the experiments)
        • Benchmark Dataset: EEG Motor Movement/Imagery Dataset.

    3. GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals

      Yimin Hou, Shuyue Jia *, Xiangmin Lun, Shu Zhang, Tao Chen, Fang Wang, Jinglei Lv

      arXiv preprint arXiv:2006.08924, 2022.
      Dynamic GNN Survey Presentation Codes Paper

      See More
        Based on the Graph Convolutional Neural Network (Graph CNN / GCN), the GCNs-Net was introduced, which filtered the EEG Motor Imagery (MI) signals considering the functional topological relationship of EEG electrodes.
        • At the subject and group level (subject-specific adaptation), 98.72% and 89.387% accuracy were achieved, respectively.
        • Pearson’s Matrix was applied to measure the correlations among channels, and represented the graph structure, i.e., graph weights and degrees.
        • Benchmark Datasets: EEG Motor Movement/Imagery Dataset, in which 20 ( a million samples), 50, 100 participants’ data were used, and the High-Gamma Dataset, in which 14 participates' data were used.

    4. Attention-based Graph ResNet for Motor Intent Detection from Raw EEG signals

      Shuyue Jia *, Yimin Hou, Yan Shi, and Yang Li
      Codes Paper

      arXiv preprint arXiv:2007.13484
      , 2022.

    5. Improving Performance: a Collaborative Strategy for the Multi-data Fusion of Electronic Nose and Hyperspectral to Track the Quality Difference of Rice

      Yan Shi, Hangcheng Yuan, Chenao Xiong, Shuyue Jia, Jingjing Liu, and Hong Men

      Sensors & Actuators: B. Chemical
      , 2021.

    6. Origin Traceability of Rice based on an Electronic Nose coupled with a Feature Reduction Strategy

      Yan Shi, Xiaofei Jia, Hangcheng Yuan, Shuyue Jia, Jingjing Liu, and Hong Men

      Measurement Science and Technology
      , 2020.

    Academic Services

    1. Student Member of IEEE

    Selected Awards

    1. 2019 Interdisciplinary Contest In Modeling

      Honorable Mention
    2. 2018 Mathematical Contest In Modeling (Jilin, China)

      First Prize