Papers
31
Total Citations
563
H-Index
13
About
Chin-Teng Lin is a pioneering researcher at the intersection of computational intelligence, brain-computer interfaces (BCIs), and autonomous robotics. His work fundamentally advances how machines interpret human intent and navigate complex environments. Lin’s major contributions include developing novel fuzzy logic systems integrated with swarm intelligence optimization—such as the fuzzy integral with particle swarm optimization for motor-imagery-based BCIs—enabling more accurate and intuitive neural control. He has also made significant strides in multirobot coordination, creating interpretable fuzzy controllers that allow robot teams to navigate cluttered spaces safely and efficiently. With highly cited works spanning object instance segmentation (66 citations), weak human preference supervision for deep reinforcement learning (58 citations), and wireless multifunctional BCIs, Lin’s research consistently pushes boundaries. His notable achievements include computational models of robot trust in human coworkers for physical human-robot collaboration, and implicit robot control using error-related potential-based BCIs. Collectively, his papers have garnered hundreds of citations, reflecting profound impact on both theoretical frameworks and practical assistive technologies. For students and researchers, Lin’s work exemplifies how blending fuzzy systems, evolutionary learning, and neural interfaces can create intelligent, human-centric autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 2A Survey on Object Instance Segmentation66 citations · 2022
- 3Weak Human Preference Supervision for Deep Reinforcement Learning58 citations · 2021
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