Xiuquan Li

Papers

Total Citations

128

H-Index

About

Xiuquan Li is a pioneering researcher at the intersection of intelligent robotics, structural health monitoring, and brain-computer interfaces (BCIs). His work spans two transformative domains: advancing autonomous infrastructure inspection through deep vision systems, and developing non-invasive neural control mechanisms for humanoid robots. Li’s most impactful contribution is his 2021 paper on an intelligent inspection robot employing deep stereo vision for three-dimensional concrete damage detection and quantification, which has garnered 86 citations. This work revolutionizes infrastructure maintenance by enabling precise, automated crack assessment in reinforced concrete structures, moving beyond traditional 2D segmentation to deliver robust, real-world 3D analysis. In the BCI field, Li introduced a continuous wavelet transform (CWT)-based method for steady-state visual evoked potential (SSVEP) classification (32 citations), and later integrated this into an asynchronous BCI system for humanoid robot control (10 citations). His research demonstrates a rare ability to bridge hardware robotics with cognitive signal processing, offering practical solutions for both civil engineering safety and assistive technology. Li’s work continues to inspire innovations in autonomous inspection and neural control systems.