Chong Hyun Lee
Papers
1
Total Citations
2
H-Index
1
About
Chong Hyun Lee is a researcher whose work lies at the intersection of biomedical signal processing and human-robot interaction. His most cited paper, "EEG Signal Classification Algorithm based on DWT and SVM for Driving Robot Control" (2015), demonstrates a focused contribution to brain-computer interfaces. In this work, Lee proposed a complete system for controlling a driving robot using EEG signals, integrating hardware like LabVIEW and DAQ with a sophisticated classification algorithm. His major contribution was the development of a method that uses Discrete Wavelet Transform (DWT) to extract frequency band features from EEG data, then applies Fisher's score to select the most discriminative features, and finally employs a Support Vector Machine (SVM) to achieve optimal classification performance for left and right directional control. While his citation count (2) is modest, the work is notable for its practical, end-to-end system design—from sensor to robot—offering a clear, replicable framework for EEG-based control. This research is particularly valuable for students and engineers exploring non-invasive BCI applications, as it provides a concrete methodology for translating neural signals into real-world robotic commands.
Research Focus
Key Achievements
Top Papers
- 1