Papers
14
Total Citations
676
H-Index
9
About
Xiaogang Chen is a leading researcher at the intersection of brain-computer interfaces (BCIs), robotics, and assistive technology, with a particular focus on improving quality of life for individuals with motor disabilities. His most significant contributions center on harnessing steady-state visual evoked potential (SSVEP)-based BCIs to enable intuitive control of robotic systems, including 7-DOF robotic arms and humanoid robots. Chen's pioneering work integrating augmented reality as a portable, flexible visual stimulator has substantially advanced the practicality of real-world BCI deployment, moving beyond the constraints of fixed computer screens. His collaborations combining computer vision with BCI control have enabled sophisticated pick-and-place robotic tasks, garnering over 150 citations each for his 2018 landmark studies. More recently, Chen has expanded into stroke rehabilitation, developing hybrid BCI-controlled soft robotic gloves, and has explored flexible electronics and stretchable strain sensors for wearable human-computer interaction. With a portfolio accumulating hundreds of citations across diverse yet interconnected domains, Chen's work represents a compelling vision of how neurotechnology and robotics can converge to empower individuals with severe physical impairments and transform rehabilitation medicine.
Research Focus
Key Achievements
Top Papers
- 1Control of a 7-DOF Robotic Arm System With an SSVEP-Based BCI156 citations · 2018
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- 9Humanoid Robot Control System Based on AR-SSVEP10 citations · 2020
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