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About

Hongfei Zhang is a leading researcher in brain–computer interfaces (BCIs), specializing in rapid serial visual presentation (RSVP) paradigms and electroencephalogram (EEG) signal processing. Their work focuses on advancing single-trial EEG classification and cross-subject detection, critical for real-world BCI applications. Zhang’s most cited paper, “An improved EEGNet for single-trial EEG classification in rapid serial visual presentation task” (2022, 30 citations), introduced a novel architecture that enhances P300 component detection, enabling faster and more accurate target recognition. This contribution has been foundational for RSVP-based BCIs. In “A review of deep learning methods for cross-subject rapid serial visual presentation detection in World Robot Contest 2022” (2023, 9 citations), Zhang systematically analyzed cross-subject models, addressing a key challenge in BCI generalization. Their recent work, “CBAM-DeepConvNet: Convolutional Block Attention Module-Deep Convolutional Neural Network for asymmetric visual evoked potentials recognition” (2025, 8 citations), further pushes boundaries by integrating attention mechanisms to boost accuracy and information transfer rates in character-spelling systems. With a growing citation impact, Zhang’s innovations in deep learning for EEG decoding are shaping the next generation of practical, high-performance BCIs.