Haojun Yin

Papers

Total Citations

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About

Haojun Yin is a rising researcher in the field of brain–computer interfaces (BCIs), with a focus on enhancing the speed and accuracy of neural signal decoding. His work centers on two key paradigms: the rapid serial visual presentation (RSVP) task and steady-state visual evoked potentials (SSVEP). In his most cited paper, "An improved EEGNet for single-trial EEG classification in rapid serial visual presentation task" (2022, 30 citations), Yin proposed a novel deep learning architecture that significantly boosts P300 detection for target image recognition, a critical step toward real-time BCI applications. His second notable contribution, "Application of Kurtosis Based Dynamic Window to Enhance SSVEP Recognition" (2022, 5 citations), introduces a statistical method to dynamically optimize EEG time windows, improving classification accuracy while reducing decision time. Together, these works address fundamental bottlenecks in BCI usability—balancing speed, reliability, and computational efficiency. Yin’s research bridges signal processing and machine learning, offering practical solutions for next-generation assistive technologies. With growing citation impact and a clear trajectory toward translational BCI systems, he is establishing himself as a promising voice in neural engineering.