LGL-BCI: A Lightweight Geometric Learning Framework for Motor Imagery-Based Brain-Computer Interfaces

no code implementations • 12 Oct 2023 • Jianchao Lu, Yuzhe Tian, Yang Zhang, Jiaqi Ge, Quan Z. Sheng, Xi Zheng

The efficiency, assessed on two public EEG datasets and two real-world EEG devices, significantly outperforms the state-of-the-art solution in accuracy ($82. 54\%$ versus $62. 22\%$) with fewer parameters (64. 9M compared to 183. 7M).

EEG Motor Imagery

DSHGT: Dual-Supervisors Heterogeneous Graph Transformer -- A pioneer study of using heterogeneous graph learning for detecting software vulnerabilities

no code implementations • 2 Jun 2023 • Tiehua Zhang, Rui Xu, Jianping Zhang, Yuzhe Tian, Xin Chen, Xiaowei Huang, Jun Yin, Xi Zheng

Vulnerability detection is a critical problem in software security and attracts growing attention both from academia and industry.

Graph Learning Vulnerability Detection

PearNet: A Pearson Correlation-based Graph Attention Network for Sleep Stage Recognition

no code implementations • 26 Sep 2022 • Jianchao Lu, Yuzhe Tian, Shuang Wang, Michael Sheng, Xi Zheng

Sleep stage recognition is crucial for assessing sleep and diagnosing chronic diseases.

Graph Attention