Papers
9
Total Citations
92
H-Index
5
About
Yunfa Fu is a leading researcher in brain-computer interfaces (BCIs), with a focus on decoding motor imagery for direct brain-controlled robotics and assistive technologies. His work centers on integrating electroencephalography (EEG) and near-infrared spectroscopy (NIRS) to capture and classify imagined movement parameters—such as hand clenching force, speed, and limb—achieving up to six-class classification in a landmark 2016 study (44 citations). Fu has pioneered the use of time-domain slow potentials and phase synchronization features to discriminate motor imagery patterns, enabling more intuitive control of prosthetic devices and intelligent cars. His research extends to visual-motor imagery paradigms for driving BCIs (2021, 6 citations) and neurofeedback-based control strategies (2018, 3 citations). With over 90 combined citations across his most-cited works, Fu’s contributions have advanced the practical deployment of BCIs in rehabilitation and human-robot interaction, notably through his comprehensive review of direct brain-controlled robot interface technology (2012). His work continues to shape the development of non-invasive, real-time neural control systems.
Research Focus
Key Achievements
Top Papers
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- 6Direct Brain-controlled Robot Interface Technology4 citations · 2012
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