Shuyue Yu

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About

Shuyue Yu is a researcher advancing the field of brain-computer interfaces (BCI) and human-robot interaction, with a focus on integrating neural and muscular signals for assistive robotics. Their most cited work, "Robotic arm control system based on brain-muscle mixed signals" (2022, 17 citations), addresses critical limitations in conventional BCI systems—specifically, single input sources, low feature recognition accuracy, and limited output commands. Yu proposes a novel hybrid approach that fuses electroencephalogram (EEG) and electromyogram (EMG) signals to create a more robust, multi-modal control framework for robotic arms. This contribution enhances the precision and versatility of prosthetic and assistive devices, offering a pathway toward more intuitive human-machine collaboration. By combining brain and muscle signals, Yu’s work improves real-time control and expands the range of executable commands, directly benefiting individuals with motor impairments. With growing interest in non-invasive neural interfaces, Yu’s research represents a meaningful step toward practical, high-performance BCI systems. Their work continues to influence the design of smarter, more adaptive robotic assistants.