Do-Yeun Lee

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About

Do-Yeun Lee is a leading researcher in non-invasive brain-computer interfaces (BCIs) and multimodal neural signal processing, with a focus on enabling natural, intuitive control of assistive robotic systems. Her most cited work, a 2020 dataset paper with 76 citations, provides a rich multimodal signal repository capturing 11 intuitive upper extremity movement tasks across multiple recording sessions. This foundational resource has become critical for training and benchmarking machine learning models that decode neural activity for prosthetic and rehabilitation technologies. Lee’s contributions address a central challenge in BCI research: bridging the gap between artificial signal matching and seamless user-robot interaction. By systematically collecting and sharing high-quality electroencephalography (EEG) and electromyography (EMG) data, she has accelerated progress toward real-world, user-friendly BCI systems. Her work is widely recognized for its rigor and practical impact, supporting the development of more adaptive and responsive neuroprosthetics. Lee’s research continues to shape how scientists and engineers approach non-invasive neural decoding, making her a key figure in the advancement of human-machine interfaces for motor rehabilitation and assistive technologies.