Dae-Hyeok Lee
Papers
2
Total Citations
117
H-Index
2
About
Dae-Hyeok Lee is a leading researcher in the field of brain-computer interfaces (BCIs), with a primary focus on decoding complex motor intentions from neural signals to enable intuitive human-robot interaction. His work centers on non-invasive EEG-based systems, particularly for upper extremity movement control. Lee’s major contributions include the development of a groundbreaking multimodal signal dataset for 11 intuitive movement tasks from a single upper extremity, recorded across multiple sessions (76 citations). This resource is critical for advancing naturalistic BCI control. He has also pioneered novel deep learning architectures for EEG classification, such as the Hierarchical Flow Convolutional Neural Network (41 citations), which significantly improves the decoding of forearm movement imagery. By addressing the challenge of accurately extracting kinematic information from brain signals, Lee’s research directly supports the creation of more responsive and human-like robotic prosthetics and assistive devices. His work is highly influential in bridging the gap between neural decoding and practical, real-world BCI applications.
Research Focus
Key Achievements
Top Papers
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