Hong Gi Yeom
Papers
7
Total Citations
100
H-Index
5
About
Hong Gi Yeom is a pioneering researcher at the forefront of brain-computer interface (BCI) and brain-machine interface (BMI) technology, with a focus on restoring motor function for individuals with disabilities. His work centers on decoding non-invasive neural signals—primarily from magnetoencephalography (MEG) and electroencephalography (EEG)—to predict and control robotic arm movements in three-dimensional space. Yeom’s most-cited study (2015, 41 citations) demonstrated the feasibility of driving a robot arm using trajectories predicted from non-invasive neural signals, a significant step toward practical assistive devices. He further advanced the field by applying LSTM deep learning models to improve the accuracy of reaching trajectory prediction from MEG signals (2020, 21 citations), addressing a critical challenge in movement prediction. Yeom has also explored user state classification via functional brain connectivity and convolutional neural networks, and contributed to 3D environmental mapping using lidar-inertial odometry for robotic navigation. His comprehensive review of BCI challenges and future trends (2022, 18 citations) synthesizes strategies for real-world BCI deployment. Through these contributions, Yeom is helping bridge the gap between neural decoding and reliable, non-invasive robotic control.
Research Focus
Key Achievements
Top Papers
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- 3Studies to Overcome Brain–Computer Interface Challenges18 citations · 2022
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- 5Trends and Future of Brain-Computer Interfaces5 citations · 2018
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