Papers
1
Total Citations
3
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1
About
Yanzhao Pan is a rising researcher at the forefront of passive Brain-Computer Interfaces (BCIs), with a focused expertise in EEG-based error detection for human-robot collaboration. Their most cited work, "Advancing passive BCIs: a feasibility study of two temporal derivative features and effect size-based feature selection in continuous online EEG-based machine error detection" (2024), introduces novel temporal derivative features and a robust effect size-based feature selection method to enhance the real-time detection of error-related potentials (ErrPs). This contribution is pivotal for enabling dynamic, adaptive interaction between humans and assistive robotic systems, directly addressing the challenge of improving BCI reliability in practical, continuous online settings. With 3 citations already for this recent publication, Pan’s work is gaining traction for its methodological rigor and practical implications. By advancing passive BCI technology, Yanzhao Pan is helping to pave the way for more intuitive and responsive human-machine interfaces, making significant strides toward seamless collaboration between humans and intelligent systems.
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Top Papers
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