Papers
9
Total Citations
261
H-Index
7
About
Kyuhwa Lee is a leading researcher at the intersection of brain-machine interfaces (BMI), assistive robotics, and human-robot interaction. His work focuses on decoding neural signals to restore mobility and independence for individuals with severe motor impairments. Lee’s major contributions include pioneering EEG-based systems for lower-limb movement onset decoding (70 citations), enabling continuous classification and asynchronous detection to trigger robotic gait trainers. He also developed a mind-controlled wheelchair for people with tetraplegia (48 citations), demonstrating that BMI skill acquisition is feasible for real-world mobility. His research extends to robot imitation learning, where he introduced probabilistic activity grammars (63 citations) to allow robots to learn reusable task components from human demonstrations. Lee has also explored adaptive human-robot collaboration in musical contexts and robust grasping strategies for under-actuated hands. His work on brain-actuated gait trainers with visual and proprioceptive feedback (30 citations) highlights his commitment to closed-loop neurorehabilitation. Through these innovations, Lee has significantly advanced the translation of BMI technology from lab to clinic, offering new hope for motor recovery and assistive mobility.
Research Focus
Key Achievements
Top Papers
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- 4Brain-actuated gait trainer with visual and proprioceptive feedback30 citations · 2017
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- 8Hierarchies for Embodied Action Perception7 citations · 2013
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