Yonghao Song
Papers
4
Total Citations
43
H-Index
3
About
Yonghao Song is a leading researcher at the intersection of brain-machine interfaces (BMI), assistive robotics, and EEG-based visual decoding. His work focuses on translating neural signals into practical, real-world control systems for rehabilitation and daily assistance. Song’s major contributions include developing a practical EEG-based human-machine interface to online control an upper-limb assist robot (29 citations), demonstrating how brain signals can directly drive robotic support for paralyzed individuals. He further advanced assistive technology by integrating shared control between BMI and computer vision for mobile robots (6 citations), enabling stroke patients to navigate their environment more independently. In rehabilitation, Song proposed a vision-based compensation detection approach during robotic stroke therapy (3 citations), offering a convenient, non-invasive method to improve motor recovery outcomes. His most recent work pushes the boundaries of neural decoding by using language-guided contrastive learning to recognize natural images from EEG signals (5 citations), addressing the challenge of low signal-to-noise ratio in non-invasive brain recordings. Through these innovations, Song is bridging the gap between neural activity and practical assistive technology, with his most cited work laying the foundation for more intuitive, responsive human-machine collaboration in healthcare.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4