Nan Duan
Papers
2
Total Citations
126
H-Index
2
About
Nan Duan is a leading researcher at the intersection of brain-computer interfaces (BCIs) and rehabilitation robotics, with a primary focus on decoding neural signals for assistive technologies. Her most influential work, a 2020 study on data augmentation for motor imagery (MI) signal classification using hybrid neural networks, has garnered 122 citations, establishing a foundational method for improving BCI accuracy in neurological rehabilitation and robot control. Duan’s contributions extend to adaptive control systems, where she pioneered a fuzzy-adaptive impedance control method for upper limb rehabilitation robots, integrating surface electromyography (sEMG) to incorporate patients’ voluntary residual motor function—a novel approach that enhances robotic responsiveness during stroke therapy. This work, though early in its citation trajectory, demonstrates her commitment to patient-centric, real-time adaptive systems. Duan’s research directly addresses critical challenges in spontaneous BCI paradigms, offering scalable solutions for signal processing and human-robot interaction. Her achievements highlight a career dedicated to translating neural and muscular signals into practical, therapeutic robotic applications, making her a pivotal figure in advancing assistive technologies for motor-impaired individuals.
Research Focus
Key Achievements
Top Papers
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- 2