Subject-Aware Multi-Granularity Alignment for Zero-Shot EEG-to-Image Retrieval
Decoding visual content from electroencephalography (EEG) is important for understanding neural visual representations and developing non-invasive brain-computer interfaces. Existing approaches mainly improve EEG representation learning and cross-modal alignment while treating pretrained visual representations as fixed supervision targets. However, pretrained vision models organize information hierarchically, with different depths encoding complementary structural and semantic information, and the visual granularity most compatible with EEG may vary across subjects. To address this issue, we propose Subject-Aware Multi-Granularity Alignment (SAMGA), which makes visual-target construction ...
| Authors | Lin Jiang, Qingshan She, Jiale Xu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 April 2026 |
| Last updated at source | 13 August 2026 |
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