Yunjun Nam

Pohang University of Science and Technology

Papers

Total Citations

122

H-Index

About

Yunjun Nam is a pioneering researcher in the field of human-machine interfaces (HMIs), with a focus on non-invasive biosignal processing and assistive robotics. His key research areas include multimodal interfaces that integrate electrooculogram (EOG), electroencephalogram (EEG), glossokinetic potential (GKP), and electromyogram (EMG) signals to enable intuitive and robust control of external devices. Nam’s major contribution is the development of the GOM-Face interface, a novel system that harnesses electrical potentials from facial movements—including tongue, eye, and muscle activity—to control humanoid robots. This work, published in 2013 and cited over 110 times, demonstrated a practical, wearable alternative to traditional joystick or keyboard controls, significantly advancing accessibility for individuals with motor impairments. In parallel, his research on hybrid EOG/ERP interfaces showed how combining eye movement signals with brain event-related potentials could enhance command accuracy and reduce user fatigue. Nam’s work has been instrumental in bridging the gap between physiological sensing and real-world robotic applications, earning recognition for its innovation in assistive technology. His contributions continue to inspire new generations of researchers exploring multimodal, biosignal-driven interaction systems.