Kibae Lee
Papers
1
Total Citations
2
H-Index
1
About
Kibae Lee is a researcher in brain-computer interfaces (BCI) and biomedical signal processing, with a focus on EEG-based control systems. Their most cited work, "EEG Signal Classification Algorithm based on DWT and SVM for Driving Robot Control" (2015), proposes a novel framework for classifying left and right directional commands from EEG signals. The system integrates discrete wavelet transform (DWT) for feature extraction, Fisher score for selecting discriminative frequency bands, and a support vector machine (SVM) classifier to optimize classification accuracy. This contribution advances real-time, non-invasive control of robotic platforms, demonstrating practical applications for assistive technology. Although the paper has received 2 citations, it represents a foundational step in EEG-driven robotics, combining signal processing and machine learning to enhance human-robot interaction. Lee’s work underscores the potential of BCI systems for autonomous navigation and rehabilitation, offering a scalable approach to decoding neural commands. Their research bridges engineering and neuroscience, contributing to the growing field of intelligent robotic control.
Research Focus
Key Achievements
Top Papers
- 1