Zian Pei

Papers

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About

Zian Pei is a leading researcher in brain–computer interfaces (BCI), with a primary focus on P300-based spelling systems and event-related potential (ERP) detection. Their major contributions center on enhancing the performance of P300 detection through advanced machine learning and deep learning architectures. Pei’s most cited work, “Performance Enhancement of P300 Detection by Multiscale-CNN” (2021, 32 citations), introduced a novel multiscale convolutional neural network that significantly improves both information transmission rate (ITR) and detection accuracy—two critical metrics for practical BCI applications. This work addresses the longstanding challenge of balancing recognition speed with reliability. In their second highly cited paper, “P300 Recognition Based on Ensemble of SVMs” (2020, 7 citations), Pei tackled the problem of detecting P300 signals with minimal repetitions, a key hurdle for real-time BCI systems. This research earned recognition through the BCI Controlled Robot Contest at the 2019 World Robot Conference, demonstrating real-world applicability. Pei’s work has advanced the field by making P300-based BCIs faster and more accurate, paving the way for more responsive assistive technologies.