Papers
5
Total Citations
89
H-Index
5
About
Shih-Chung Chen is a leading researcher at the intersection of brain-computer interfaces (BCIs) and assistive robotics, with a particular focus on improving quality of life for individuals with motor neuron disease. His core contributions center on developing single-channel SSVEP-based BCI systems that enable intuitive control of robotic devices through visual evoked potentials. Chen pioneered the integration of fuzzy decision algorithms with BCI technology, as demonstrated in his highly cited 2017 work on a maze game interface (39 citations) and his 2016 study on an automatic feeding robot (18 citations). His innovative approach allows users to navigate complex environments and control assistive robots using just four commands—counterclockwise, clockwise, forward, and backward—processed through a single EEG channel. Beyond BCI, Chen has contributed to underwater robotics, developing PID controllers for station-keeping in surveillance applications. His work on interactive autonomous meal-assistance robots (2015) exemplifies his commitment to practical, life-enhancing technologies. With a publication record spanning from 2015 to 2017, Chen’s research has accumulated over 89 citations, establishing him as a notable figure in applied BCI and human-robot interaction.
Research Focus
Key Achievements
Top Papers
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- 3A new SSVEP based BCI application on the mobile robot in a maze game15 citations · 2016
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- 5A PID controller for the underwater robot station-keeping8 citations · 2016