Kaifan Zhang — accepted AI-conference papers

2026

Shrinking the Teacher: An Adaptive Teaching Paradigm for Asymmetric EEG-Vision Alignment

Lukun Wu, Jie Li, Ziqi Ren, Kaifan Zhang, Xinbo Gao

AAAI 2026technical

Decoding visual features from EEG signals is a central challenge in neuroscience, with cross-modal alignment as the dominant approach. We argue that the relationship between visual and brain modalities is fundamentally asymmetric, characterized by two critical gaps: a Fidelity Gap (stemming from EEG

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2025

CognitionCapturer: Decoding Visual Stimuli from Human EEG Signal with Multimodal Information

Kaifan Zhang, Lihuo He, Xin Jiang, Wen Lu, Di Wang, Xinbo Gao

AAAI 2025technical

Electroencephalogram (EEG) signals have attracted significant attention from researchers due to their non-invasive nature and high temporal sensitivity in decoding visual stimuli. However, most recent studies have focused solely on the relationship between EEG and image data pairs, neglecting the va…

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