Rongxiao Guo
Papers
2
Total Citations
15
H-Index
2
About
Rongxiao Guo is a researcher at the forefront of brain-computer interface (BCI) technology, specializing in integrating multimodal control systems and deep learning for electroencephalography (EEG) analysis. His work primarily focuses on enhancing the practicality and autonomy of BCI systems, addressing key limitations such as low flexibility and user fatigue. In a notable 2023 study, Guo developed an asynchronous robotic arm control system that combines eye-tracking with steady-state visual evoked potentials (SSVEP) in a virtual reality environment, achieving 13 citations for its innovative simultaneous and sequential control modes. This work significantly advances real-world BCI applications by improving user interaction and reducing cognitive load. Additionally, Guo tackled the challenge of cross-subject EEG classification in rapid serial visual presentation (RSVP) systems with his 2022 multi-scale EEGNet approach, which trains subject-agnostic models to generalize across individuals. Though early in its impact with 2 citations, this contribution addresses a critical hurdle in deploying BCI systems for diverse users. Guo’s research demonstrates a clear trajectory toward more intuitive, adaptable, and user-friendly neural interfaces, positioning him as an emerging leader in applied BCI engineering.
Research Focus
Key Achievements
Top Papers
- 1
- 2A multi-scale EEGNet for cross-subject RSVP-based BCI system2 citations · 2022