Chengwei Lei

California State University, Bakersfield

Papers

Total Citations

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About

Chengwei Lei is a pioneering researcher at the intersection of brain-computer interfaces (BCI) and intelligent robotics, with a primary focus on fusing human cognition with machine autonomy. His most influential work, “A Brain–Robot Interaction System by Fusing Human and Machine Intelligence” (2019, 46 citations), introduces a groundbreaking hybrid BCI framework that integrates P300 and steady-state visual evoked potential (SSVEP) signals to decode human intention in real time. By coupling this neural decoding with machine intelligence, Lei’s system dramatically improves the responsiveness and accuracy of brain-controlled robots—a critical advancement for assistive technologies and human-robot collaboration. His contributions demonstrate how synergistic human-machine fusion can overcome the limitations of traditional BCI, enabling smoother, more intuitive control. This work has been widely cited in the fields of neural engineering and robotics, reflecting its impact on both theoretical frameworks and practical applications. Lei’s research continues to push the boundaries of how humans and machines can seamlessly cooperate, offering promising pathways for next-generation neuroprosthetics and intelligent automation.