Papers
10
Total Citations
207
H-Index
6
About
Yadong Liu is a researcher whose work spans brain-computer interfaces (BCIs), assistive robotics, and reinforcement learning, with a particular focus on developing technologies that enhance human-machine interaction for individuals with physical disabilities. His most influential contribution, "Towards BCI-Actuated Smart Wheelchair System" (2018), has garnered 118 citations and represents a landmark effort in translating electroencephalogram-based neural signals into practical mobility solutions. Liu has pioneered diverse BCI paradigms, including visual ERP, tactile P300, SSVEP, and motor imagery approaches, demonstrating a comprehensive command of neural signal processing techniques. His innovative Morse code-inspired multiclass motor imagery framework (2015, 32 citations) exemplifies his creative approach to expanding BCI communication bandwidth. Beyond assistive technology, Liu has extended his expertise into machine learning, exploring attention-augmented contrastive learning for reinforcement learning state representation (2020, 16 citations) and addressing multi-goal exploration challenges. His applied work encompasses BCI-controlled robotic arms and omnidirectional mobile platforms, bridging neuroscience and robotics engineering. Collectively, Liu's research portfolio reflects a sustained commitment to empowering disabled individuals through intelligent, brain-driven systems while advancing the theoretical foundations of adaptive machine learning.
Research Focus
Key Achievements
Top Papers
- 1Towards BCI-actuated smart wheelchair system118 citations · 2018
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- 4An Tactile ERP-Based Brain–Computer Interface for Communication10 citations · 2018
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- 6Toward Brain-Actuated Mobile Platform7 citations · 2018
- 7Fast robot arm control based on brain-computer interface6 citations · 2016
- 8A 3D Visual Stimuli Based P300 Brain-computer Interface4 citations · 2017
- 9
- 10Tactile P300 Brian-Computer Interface Paradigm for Robot Arm Control2 citations · 2018