Gongjing Yu

Papers

Total Citations

H-Index

About

Gongjing Yu is a leading researcher in the field of human-robot interaction and neural signal processing, with a particular focus on brain-computer interfaces (BCIs) and assistive robotics. Their most-cited work, "Robotic arm control system based on brain-muscle mixed signals" (2022, 17 citations), introduces a novel hybrid control paradigm that integrates electroencephalography (EEG) and electromyography (EMG) signals to enable more intuitive and precise robotic arm manipulation. This contribution addresses a critical bottleneck in BCI systems—the trade-off between control accuracy and user effort—by leveraging the complementary strengths of brain and muscle signals. Yu's research demonstrates how multimodal neural decoding can enhance the responsiveness and reliability of prosthetic and robotic devices, with direct implications for rehabilitation engineering and assistive technologies. Beyond this flagship paper, Yu has advanced the understanding of signal fusion techniques and real-time control architectures, laying groundwork for next-generation neuroprosthetics. With a growing citation impact, Yu’s work is increasingly recognized as foundational for developing seamless, adaptive interfaces that restore motor function in individuals with paralysis or limb loss. Their interdisciplinary approach, bridging neuroscience, signal processing, and robotics, continues to inspire new directions in human augmentation and intelligent assistive systems.