Yun-Ting Kuo
Papers
1
Total Citations
3
H-Index
1
About
Yun-Ting Kuo is a rising researcher in the field of brain-computer interfaces (BCIs), with a focused expertise in electroencephalography (EEG) signal processing. Her work centers on the challenging task of translating motor imagery commands into device control, particularly addressing the automatic design of spectral and spatial filters—a critical hurdle in BCI development. In her most-cited paper, "Simultaneously Spatiospectral Pattern Learning and Contaminated Trial Pruning for Electroencephalography-Based Brain Computer Interface" (2020, 3 citations), Kuo proposes a novel framework that jointly learns optimal spatiospectral patterns while pruning contaminated EEG trials. This dual approach enhances the robustness and accuracy of BCI systems, directly tackling the difficulty of selecting frequency bands for spectral filters. Though early in her career, Kuo’s contributions are significant for advancing practical, real-time BCI applications, such as controlling robotic arms. Her work is particularly notable for integrating pattern learning with data quality improvement, a step toward more reliable neural interfaces. As her citation count grows, Kuo’s research promises to impact both assistive technology and neural engineering, offering a streamlined path from brain signals to machine commands.
Research Focus
Key Achievements
Top Papers
- 1