Papers
10
Total Citations
129
H-Index
6
About
Yu-Kai Wang is a dynamic researcher whose work sits at the compelling intersection of brain-computer interfaces (BCIs), human-robot collaboration, and intelligent autonomous systems. Best known for pioneering portable, real-world BCI applications, Wang's most influential contribution is a wireless multifunctional SSVEP-based assistive system (2018, 43 citations) that addressed a critical gap in mobility and practicality for patients with severe motor impairments. His early work also includes a dedicated BCI-powered eating assistive device (2017), underscoring his commitment to translating neurotechnology into meaningful quality-of-life improvements. Wang's research evolved to explore the neural underpinnings of human-robot interaction, particularly cognitive conflict during physical collaboration. Through studies leveraging error-related and prediction-error negativity signals, he has developed methods to detect when humans experience surprise or uncertainty while working alongside robots — insights that can directly inform more intuitive robotic behavior. His 2022 work on implicit robot control using error-related potentials (19 citations) exemplifies this trajectory. More recently, Wang has expanded into human-autonomous teaming, developing trust-modelling frameworks that incorporate real-time human states. With over 120 cumulative citations, his body of work offers foundational contributions for researchers building safer, smarter, and more human-centered collaborative systems.
Research Focus
Key Achievements
Top Papers
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- 6Prediction Error Negativity in Physical Human-Robot Collaboration8 citations · 2020
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- 10A Multi-task Scheduling Algorithm for Cloud Robots2 citations · 2019