Xiaokang Shu
Papers
5
Total Citations
233
H-Index
5
About
Xiaokang Shu is a prominent researcher specializing in brain-computer interfaces (BCIs), human-robot interaction, and assistive neurotechnology. His work centers on developing practical, non-invasive BCI systems that empower individuals with motor impairments to control robotic devices using neural signals derived from electroencephalography (EEG). Shu's most influential contribution lies in pioneering shared control frameworks for BCI-driven robotic arms, where he elegantly combines human neural intent with computer vision to overcome the inherent limitations of EEG signal quality. His 2019 paper on shared robotic arm control has garnered over 103 citations, establishing him as a key voice in the field, while subsequent work on multi-object reach-and-grasp strategies (54 citations) and hybrid BCI architectures (61 citations) demonstrates consistent, high-impact output across multiple years. Beyond robotic manipulation, Shu has explored practical rehabilitation applications, designing wearable BCI systems like eConHand for stroke recovery, reflecting a commitment to accessible, real-world solutions. His earlier investigations into steady-state visual evoked potentials (SSVEPs) and stimulus optimization further reveal the breadth of his foundational expertise. Collectively, Shu's research bridges neuroscience and robotics, offering meaningful pathways toward greater independence for people living with neurological disabilities.
Research Focus
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Top Papers
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