Qinyi Sun

Papers

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About

Qinyi Sun’s research lies at the intersection of brain-computer interfaces (BCI), exoskeleton robotics, and deep learning, with a focus on decoding human movement intention for assistive technologies. Their most cited work, “RP-based Voluntary Movement Intention Detection of Lower limb using CNN” (2020), addresses a critical challenge in rehabilitation robotics: the low accuracy of detecting voluntary movement intentions from neural signals. By leveraging recurrence plots (RP) and convolutional neural networks (CNN), Sun proposed a novel deep learning framework that significantly improves the detection of lower-limb movement intent, enabling more responsive and natural control of exoskeleton devices. This contribution bridges the gap between raw neural data and practical robotic assistance, offering a pathway toward more intuitive human-robot interaction. With 4 citations, this foundational paper has influenced subsequent research in BCI-driven rehabilitation. Sun’s work exemplifies how combining signal processing with deep learning can advance neuroprosthetics, making assistive technologies more adaptive and user-centered—a vital step toward restoring mobility for individuals with motor impairments.