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About

Xiaoyan Deng is a leading researcher in the field of brain-machine interfaces (BMI) and intelligent robotic control, with a particular focus on assistive technologies. Her most impactful work centers on developing shared control frameworks that seamlessly integrate human intent with robotic autonomy, especially for brain-actuated wheelchair systems. In her highly cited 2020 paper (67 citations), Deng introduced a Bayesian shared control approach that intelligently combines automatic robot control with brain-actuated commands, effectively managing the inherent uncertainty in both human neural signals and robotic perception. She further advanced this area with a self-adaptive shared control system (31 citations) that uses a brain state evaluation network to dynamically balance control authority between the user and the robot, addressing a critical challenge in BCI-based systems. Deng has also contributed to practical robotic applications, including SSVEP-based BCI control for high-degree-of-freedom manipulators and vision-based systems for industrial robot picking. Her work on dynamic obstacle avoidance using polar coordinates and laser radar has improved mobile robot safety in unknown environments. Through her innovative integration of Bayesian methods and neural signal processing, Deng has significantly advanced the practicality and reliability of brain-controlled assistive robots.