Xiaoqin Lian

Beijing Technology and Business University

Papers

Total Citations

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About

Xiaoqin Lian is an emerging researcher at the intersection of brain-computer interfaces (BCI) and robotics, with a primary focus on decoding neural signals for real-world robotic control. Her most cited work, "A Robot Control Method based on Motor Imagery EEG Signals" (2023), pioneers a framework that translates imagined motor commands—such as limb movements—directly into robotic actions, bypassing traditional physical input. This contribution addresses a critical challenge in assistive technology: enabling seamless, intuitive control for individuals with motor impairments. By leveraging electroencephalography (EEG) to interpret motor imagery, Lian’s method enhances the responsiveness and accuracy of human-robot interaction systems, a key step toward practical BCI-driven prosthetics and rehabilitation tools. Though her citation count is currently modest (2 citations for this paper), her work signals a growing interest in non-invasive neural interfaces. Lian’s research sits at the forefront of a rapidly expanding field, where her approach could influence future designs for adaptive, user-centered robotic systems. As BCI technology matures, her contributions are poised to gain recognition for bridging cognitive intent and mechanical action.