Chuanqi Tan
Papers
9
Total Citations
3,006
H-Index
7
About
Chuanqi Tan is a leading researcher whose work bridges two transformative fields: deep transfer learning and brain-computer interfaces (BCI) for rehabilitation robotics. His seminal survey, "A Survey on Deep Transfer Learning" (2018), has amassed over 2,857 citations, establishing itself as a foundational reference for researchers seeking to adapt deep learning models across domains with limited labeled data—a critical challenge in fields like bioinformatics and robotics. Beyond this landmark contribution, Tan has pioneered innovative approaches to BCI-driven robotic control, addressing the fundamental difficulty of translating noisy neural signals into precise, high-degree-of-freedom movements. His work on autoencoder-based transfer learning for rehabilitation robots (2019) and shared control strategies using Fused Fuzzy Petri Nets (2018) has significantly advanced the practicality of brain-actuated grasping for assistive technologies. Tan’s research also tackles asynchronous BCI paradigms and hybrid EEG-based systems, enabling more natural, user-driven control of robotic arms and hands. By combining transfer learning techniques with BCI architectures, he has opened new pathways for creating robust, real-world rehabilitation systems that can adapt to individual users, making his contributions equally impactful in machine learning theory and applied neurorehabilitation.
Research Focus
Key Achievements
Top Papers
- 1A Survey on Deep Transfer Learning2,857 citations · 2018
- 2A Survey on Deep Transfer Learning36 citations · 2018
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- 4Asynchronous brain-computer interface shared control of robotic grasping23 citations · 2019
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- 7A hybrid EEG-based BCI for robot grasp controlling16 citations · 2017
- 8An Asynchronous Mi-Based BCI for Brain-Actuated Robot Grasping Control3 citations · 2017
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