Zequan Lian

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About

Zequan Lian is a rising researcher in the field of brain-computer interfaces (BCIs), with a primary focus on steady-state visual evoked potential (SSVEP) recognition. His work addresses a critical challenge in BCI technology: achieving high accuracy in short time windows. In his most-cited paper, "Application of Kurtosis Based Dynamic Window to Enhance SSVEP Recognition" (2022, 5 citations), Lian introduced an innovative method that uses kurtosis—a statistical measure of signal peakedness—to dynamically optimize the time window for SSVEP decoding. This approach improves the trade-off between speed and accuracy, a persistent bottleneck in real-time BCI systems. By enabling more reliable decision-making from electroencephalogram (EEG) signals, his contribution has practical implications for assistive communication devices and neural prosthetics. Though early in his career, Lian’s work demonstrates a strong grasp of signal processing and its application to neurotechnology. His research stands out for its focus on dynamic, data-driven solutions that adapt to individual neural variability, paving the way for more responsive and user-friendly BCI systems. As the field pushes toward practical, everyday use, Lian’s contributions offer a promising direction for enhancing SSVEP-based interaction.