Yeji Lee
Papers
1
Total Citations
4
H-Index
1
About
Yeji Lee is a rising researcher in the field of brain-computer interfaces (BCI), with a focused expertise in decoding neural signals for motor rehabilitation. Her work centers on the intersection of electroencephalography (EEG) signal processing and deep learning, specifically addressing the challenge of classifying real and imagined knee movements. In her most-cited study, Lee demonstrated how deep learning architectures can effectively distinguish between executed and imagined lower-limb motor tasks, a critical step toward developing practical, non-invasive BCI systems for patients with motor impairments. This research, which has already garnered early citations, tackles the persistent problem of EEG signal contamination by leveraging the relative stability of motor imagery data. By advancing the classification of lower-limb movements—an area often overshadowed by hand and arm control in BCI research—Lee is contributing to more holistic neuroprosthetic and rehabilitation technologies. Her work holds promise for restoring mobility and improving quality of life for individuals with paralysis or limb loss.
Research Focus
Key Achievements
Top Papers
- 1