Papers
2
Total Citations
8
H-Index
2
About
Jialin Xu is a researcher at the forefront of neural engineering and rehabilitation robotics, specializing in brain-computer interfaces (BCIs) and surface electromyography (sEMG) for upper-limb motor recovery. Their work bridges the gap between human neural signals and robotic assistive technologies, with a particular focus on stroke rehabilitation. Xu’s most cited paper, "A Multi-Scale Temporal Convolutional Network with Attention Mechanism for Force Level Classification during Motor Imagery of Unilateral Upper-Limb Movements" (2023, 5 citations), introduces a novel deep learning architecture that decodes imagined force levels from EEG signals—a critical step toward intuitive, brain-controlled robotic therapy that adapts to a patient’s intended effort. This work addresses the challenge of translating static motor imagery paradigms to dynamic human-robot interaction. Earlier foundational research, "The sEMG characteristics of human upper limb during circle drawing on EULRR system" (2017, 3 citations), characterized muscle activation patterns during robot-assisted drawing tasks, providing essential physiological data for designing task-specific training protocols. By combining advanced signal processing with clinical insight, Xu is helping to create more responsive, personalized rehabilitation systems that can restore motor function after stroke.
Research Focus
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Top Papers
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