Xiaojia Wang

Clemson University, Taiyuan University of Technology

Papers

Total Citations

H-Index

About

Xiaojia Wang is a leading researcher at the intersection of human–machine interaction, sensor fusion, and intelligent perception systems. Her work is distinguished by pioneering contributions to brain–computer interfaces (BCI) and multi-modal sensor calibration, with a focus on enabling seamless collaboration between humans and autonomous systems. Wang’s most cited paper, "Brain–Computer Interface Integrated With Augmented Reality for Human–Robot Interaction" (2022, 43 citations), introduces a novel SSVEP-based EEG paradigm that dramatically improves the stability and efficiency of BCI-driven robotic control, laying the groundwork for intuitive, hands-free operation in assistive and industrial robotics. More recently, her 2024 study on "Camera LiDAR calibration" (6 citations) presents an automatic, high-accuracy method using novel PLE metrics, addressing a critical bottleneck in sensor fusion for autonomous vehicles and augmented reality. Wang’s work is notable for its practical impact: her calibration techniques directly enhance the reliability of perception systems, while her BCI research opens new pathways for neuroadaptive interfaces. With a growing citation footprint and a focus on real-world deployment, Xiaojia Wang is shaping the future of human–robot collaboration and autonomous perception.