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npx versuz@latest install hiyenwong-ai-collection-collection-skills-eeg-vision-multimodal-eeg-basedgit clone https://github.com/hiyenwong/ai_collection.gitcp ai_collection/SKILL.MD ~/.claude/skills/hiyenwong-ai-collection-collection-skills-eeg-vision-multimodal-eeg-based/SKILL.md--- name: eeg-vision-multimodal-eeg-based description: "Reconstructing visual stimuli from non-invasive electroencephalography (EEG) remains challenging due to its low spatial resolution and high noise, particularly under realistic low-..." --- # EEG2Vision: A Multimodal EEG-Based Framework for 2D Visual Reconstruction in Cognitive Neuroscience ## Overview Reconstructing visual stimuli from non-invasive electroencephalography (EEG) remains challenging due to its low spatial resolution and high noise, particularly under realistic low-density electrode configurations. To address this, we present EEG2Vision, a modular, end-to-end EEG-to-image framework that systematically evaluates reconstruction performance across different EEG resolutions (128, 64, 32, and 24 channels) and enhances visual quality through a prompt-guided post-reconstruction boosting mechanism. Starting from EEG-conditioned diffusion reconstruction, the boosting stage uses a multimodal large language model to extract semantic descriptions and leverages image-to-image diffusion to refine geometry and perceptual coherence while preserving EEG-grounded structure. Our experiments show that semantic decoding accuracy degrades significantly with channel reduction (e.g., 50-way Top-1 Acc from 89% to 38%), while reconstruction quality slight decreases (e.g., FID from 76.77 to 80.51). The proposed boosting consistently improves perceptual metrics across all configurations, achieving up to 9.71% IS gains in low-channel settings. A user study confirms the clear perceptual preference for boosted reconstructions. The proposed approach significantly boosts the feasibility of real-time brain-2-image applications using low-resolution EEG devices, potentially unlocking this type of applications outside laboratory settings. ## Source - **arXiv:** 2604.08063v1 - **Date:** 2026-04-09 - **Authors:** Emanuele Balloni, Emanuele Frontoni, Chiara Matti et al. - **Categories:** cs.CV ## Methods - Computational neuroscience ## Applications - Brain data analysis and neural modeling - Neuroscience research and development ## References Emanuele Balloni et al. arXiv:2604.08063v1 ## Keywords - neuroscience - cs.CV