Structure-Guided Diffusion Model for EEG-Based Visual Cognition Reconstruction
Objective: Decoding visual information from electroencephalography (EEG) is an important problem in neuroscience and brain-computer interface (BCI) research. Existing methods are largely restricted to natural images and categorical representations, with limited capacity to capture structural features and to differentiate objective perception from subjective cognition. We propose a Structure-Guided Diffusion Model (SGDM) that incorporates explicit structural information for EEG-based visual reconstruction. Approach: SGDM is evaluated on the Kilogram abstract visual object dataset and the THINGS natural image dataset using a two-stage generative mechanism. The framework combines a structura...
| Authors | Yongxiang Lian, Yueyang Cang, Pingge Hu et al. |
|---|---|
| Primary category | cs.NE |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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