Do-Yeun Lee
Papers
1
Total Citations
6
H-Index
1
About
Do-Yeun Lee is a leading researcher in brain–machine interfaces (BMI) and neural signal processing, with a focus on decoding human motor intent for assistive and rehabilitative technologies. Her work centers on using electroencephalography (EEG) to classify upper limb movements, enabling intuitive control of robotic prosthetics and exoskeletons. In her highly cited 2020 study, Lee introduced a novel convolutional neural network architecture incorporating a 3D Inception block, which significantly improved the accuracy and robustness of movement intention detection from EEG signals. This contribution has been foundational for developing real-time, non-invasive BMI systems that bridge the gap between neural activity and external devices. With over 6 citations on this key paper alone, her research is gaining traction in both clinical and engineering communities. Lee’s work not only advances human–machine interaction but also holds promise for restoring mobility in patients with motor impairments, making her a rising voice in the intersection of artificial intelligence and neurorehabilitation.
Research Focus
Key Achievements
Top Papers
- 1