Brain-to-Image Retrieval and Reconstruction via Multimodal EEG Alignment
We present a brain-to-image system that decodes visual stimuli from EEG signals recorded during natural image viewing. Our system addresses two tasks: (1) EEG-to-image retrieval, which ranks the correct stimulus image among 200 candidates given an EEG segment, and (2) EEG-to-image reconstruction, which generates an image consistent with the perceived stimulus. For retrieval, we implement a multi-level blurring approach improved with biologically inspired EVNet features and trained with the InfoNCE loss. Evaluated over 10 random seeds for a single subject, the retrieval model achieves a mean final-epoch Top-1 accuracy of 86.30% and Top-5 accuracy of 98.55%. For reconstruction, we implement...
| Authors | Chi Kit Wong, Yan Liu, Haowen Yan |
|---|---|
| Primary category | cs.CV |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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