Papers
7
Total Citations
49
H-Index
4
About
Minji Lee is a multidisciplinary robotics and neurotechnology researcher whose work spans brain-computer interfaces (BCIs), robotic manipulation, and neurorehabilitation engineering. Her most impactful contributions lie at the intersection of EEG-based human-machine interaction and intelligent robotic systems, with a particular focus on improving the lives of individuals with motor impairments. Her most-cited work, "Multi-Task Heterogeneous Ensemble Learning-Based Cross-Subject EEG Classification Under Stroke Patients" (2024, 22 citations), advances motor imagery-based BCIs for stroke rehabilitation, addressing the persistent challenge of cross-subject variability in neural signals. Complementing this, her research on iteratively calibratable networks for robotic arm control and functional connectivity-guided deep learning demonstrates a commitment to making BCIs practically deployable in real-world settings. Beyond neural interfaces, Lee has made notable contributions to robotic perception and manipulation, including differentiable contact feature estimation for uncertain pose tracking and fast simulation frameworks for tight-tolerance assembly tasks. Her earlier work on large-scale painting robots and vision-aided mobile systems further reflects her breadth in autonomous robotics. With over 40 cumulative citations across diverse high-impact topics, Lee represents a rising voice bridging neuroscience, rehabilitation technology, and intelligent robotics.
Research Focus
Key Achievements
Top Papers
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- 4Large Size Painting with Infraless Vision-aided Mobile Robot4 citations · 2018
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