Canguang Lin

About

Canguang Lin is a leading researcher in the field of brain-computer interfaces (BCI) and assistive robotics, with a particular focus on human-robot collaboration and shared control systems. His work centers on developing intelligent frameworks that seamlessly integrate human neural commands with autonomous robotic assistance, addressing one of the most critical challenges in BCI-driven mobility aids. Lin’s most impactful contribution is his pioneering “Bayesian Shared Control Approach for Wheelchair Robot With Brain Machine Interface” (2020, 67 citations), which introduced a probabilistic method to optimally fuse human intent with robot autonomy while accounting for perceptual and action uncertainty. He further advanced the field with his “Self-adaptive shared control with brain state evaluation network” (2020, 31 citations), a novel system that dynamically adjusts control weights between human and robot based on real-time assessment of the operator’s cognitive state. His work also includes developing an SSVEP-based BCI for controlling a 4-DOF robotic manipulator (2019), demonstrating the extension of his shared control principles to high-degree-of-freedom systems. Lin’s research is highly influential in shaping next-generation assistive technologies that are both safe and responsive to user needs.

Research Focus

Key Achievements

Top Papers

1. 1
   A Bayesian Shared Control Approach for Wheelchair Robot With Brain Machine Interface

   67 citations · 2020
2. 2
   Self-adaptive shared control with brain state evaluation network for human-wheelchair cooperation

   31 citations · 2020
3. 3
   A SSVEP-Based BCI for Controlling a 4-DOF Robotic Manipulator

   3 citations · 2019

Key Collaborators

Contact & Links