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About

Zhouyu Ji is a rising researcher in the field of brain–computer interfaces (BCI), with a primary focus on visual evoked potentials (VEPs) and deep learning methods for EEG signal processing. Their work spans steady-state visual evoked potentials (SSVEP), asymmetric visual evoked potentials (aVEPs), and rapid serial visual presentation (RSVP) paradigms, addressing key challenges in cross-subject generalization and real-time recognition. Ji’s major contributions include the development of the CBAM-DeepConvNet, an innovative architecture combining convolutional block attention modules with deep convolutional neural networks to significantly improve both accuracy and information transfer rate in character-spelling systems. Their work on kurtosis-based dynamic windowing has also advanced SSVEP recognition by enabling faster, more reliable decisions from EEG data. With their most-cited papers accumulating over 20 citations since 2022, Ji’s research is gaining traction in the BCI community. Notably, their review of deep learning methods for cross-subject RSVP detection, published in connection with the World Robot Contest 2022, provides a comprehensive benchmark for future studies. Ji’s work is particularly valuable for students and researchers seeking practical, high-performance solutions for non-invasive BCI systems.