Guangye Li
Papers
4
Total Citations
107
H-Index
3
About
Guangye Li is pioneering the development of brain-machine interfaces (BMIs) that restore motor function to paralyzed individuals. His research centers on decoding hand and arm movements from neural signals, with a particular focus on non-invasive and intracranial electroencephalography (EEG). Li’s most cited work introduces a brain-actuated robotic arm system that combines a non-invasive hybrid BCI with shared control strategies, achieving 61 citations for its practical approach to overcoming poor EEG signal quality. He has also made significant contributions using stereo-electroencephalography (SEEG), demonstrating that deep brain structures, including the posterior parietal cortex and central sulcus, encode hand-movement-related neural activity. His 2017 study on prosthetic hand control via SEEG (25 citations) and his 2020 work on enhancing gesture decoding (19 citations) highlight his ability to improve BMI performance by targeting these regions. Li’s research bridges fundamental neuroscience and applied engineering, offering hope for severely paralyzed patients to regain functional independence through advanced neural interfaces.
Research Focus
Key Achievements
Top Papers
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