Zheng Zeng
Papers
1
Total Citations
6
H-Index
1
About
Zheng Zeng is a leading researcher in biomedical signal processing and human-computer interaction (HCI), with a primary focus on electrooculography (EOG)-based technologies. His most impactful work, the 2024 paper "Residual Self-Calibrated Network With Multi-Scale Channel Attention for Accurate EOG-Based Eye Movement Classification," introduces a novel deep learning architecture that significantly improves the accuracy of eye movement classification—a critical step for applications such as assistive robots and augmented reality gaming. By integrating residual self-calibration and multi-scale channel attention mechanisms, Zeng’s model addresses longstanding challenges in EOG signal noise and variability, achieving state-of-the-art performance with 6 citations in its first year. This contribution underscores his role in advancing practical, real-time EOG-HCI systems, bridging the gap between algorithmic innovation and industrial deployment. Zeng’s work not only enhances the reliability of eye-tracking interfaces for disabled users but also opens new avenues for immersive AR experiences. His research exemplifies how targeted deep learning techniques can transform raw physiological signals into precise, actionable commands, marking him as a rising authority in assistive technology and neural interface design.
Research Focus
Key Achievements
Top Papers
- 1