Papers
1
Total Citations
5
H-Index
1
About
Xinxiu Xu is a pioneering researcher in brain-machine interfaces (BMIs), with a focus on translating neural signals into precise motor control for assistive robotics. Her most cited work, "Generative Decoding of Intracortical Neuronal Signals for Online Control of Robotic Arm to Intercept Moving Objects" (2020), introduces a novel generative decoding framework that departs from traditional discriminative algorithms. Instead of continuously converting neural spike trains into motor variables, Xu’s approach leverages generative models to improve the accuracy and fluidity of robotic arm control, enabling real-time interception of moving objects. This contribution addresses a critical bottleneck in BMI technology—enhancing responsiveness in dynamic environments—and has garnered 5 citations as a foundational step toward more intuitive neuroprosthetics. Xu’s research integrates computational neuroscience, machine learning, and neural engineering, pushing the boundaries of how intracortical signals can be harnessed for practical applications. Her work holds promise for restoring motor function in paralyzed individuals, and her innovative decoding strategy marks a notable achievement in the field, offering a pathway to more adaptive and naturalistic control of assistive devices.
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Top Papers
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