Longting Chen

Papers

Total Citations

132

H-Index

About

Longting Chen is a leading researcher at the intersection of brain-computer interfaces (BCIs) and human-robot interaction, with a primary focus on motor imagery (MI) signal processing and human action recognition. Their most impactful work, "Data Augmentation for Motor Imagery Signal Classification Based on a Hybrid Neural Network" (2020), has garnered 122 citations, introducing a novel hybrid neural network approach that significantly improves MI-based BCI performance—a critical advancement for neurological rehabilitation and robotic control systems. Chen’s contributions extend to real-time human action recognition, as demonstrated in their 2017 paper (10 citations), which proposed a fast, kinematic similarity-based method using 3D pose data for computer-robotic interfaces. This work bridges the gap between pattern recognition and practical robotic applications. By tackling key challenges in signal classification and motion analysis, Chen has established a strong foundation for more intuitive, responsive human-machine systems. Their research not only advances theoretical understanding but also offers tangible solutions for assistive technologies and autonomous robotics, making their work highly relevant for students and researchers exploring next-generation interfaces.