Xiaohe Chen

Chinese Academy of Sciences, Tianjin University

Papers

Total Citations

H-Index

About

Xiaohe Chen is a researcher at the intersection of autonomous robotics and brain-computer interfaces (BCIs), with key contributions in lightweight localization and high-frequency neural decoding. In their most cited work, "A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks" (2021, 20 citations), Chen introduced SORLA, a real-time system that uses artificial reflectors to compensate for LiDAR odometer drift during high-speed or sharp-turning maneuvers. This feature-matching approach offers a computationally efficient solution for robust robot navigation. More recently, Chen advanced BCI technology with "High-Frequency SSVEP-BCI With Row-Column Dual-Frequency Encoding and Decoding Strategy for Reduced Training Data" (2024, 4 citations), addressing a critical challenge in steady-state visual evoked potential (SSVEP) systems. By employing high-frequency visual stimuli, this work reduces visual fatigue while maintaining high accuracy and information transfer rates, and its dual-frequency encoding strategy minimizes the need for extensive training data—a significant step toward practical, user-friendly BCIs. Chen’s research demonstrates a versatile ability to solve real-world engineering problems across robotics and neural interfaces, making their work valuable for students and researchers in autonomous systems and human-computer interaction.