Byoung-Hee Kwon
Papers
3
Total Citations
19
H-Index
2
About
Byoung-Hee Kwon is a leading researcher in brain–computer interfaces (BCI) and assistive robotics, with a focus on non-invasive electroencephalogram (EEG) control for neurorehabilitation. Her work centers on developing intelligent, vision-guided robotic arm systems that can be operated through motor and visual imagery, enabling intuitive, hands-free control for individuals with motor impairments. Kwon’s most-cited paper, “Assistive Robotic Arm Control based on Brain-Machine Interface with Vision Guidance using Convolution Neural Network” (2019, 15 citations), pioneered the integration of deep learning with endogenous BCI paradigms to enhance dexterous manipulation without external stimuli. She further advanced the field by introducing visual imagery-based control in “Channel Optimized Visual Imagery based Robotic Arm Control under the Online Environment” (2023) and by classifying visual motion imagery using functional connectivity and deep neural networks (2021). These contributions demonstrate her impact in making BCI systems more practical, accurate, and responsive for real-world healthcare applications. Kwon’s research bridges neuroscience, machine learning, and robotics, offering promising pathways toward accessible assistive technologies that restore independence and improve quality of life for patients with severe motor disabilities.
Research Focus
Key Achievements
Top Papers
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