Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs
The paper introduces Generative Visual Grounding (GVG), a framework aimed at enhancing understanding of EEG signals through visual representation. By utilizing an EEG-to-image generative model, GVG allows for better interpretation of non-visual EEG data by creating structured visual contexts. The results indicate that this approach significantly improves EEG understanding and visual generation compared to traditional methods.
- ▪Generative Visual Grounding (GVG) uses an EEG-to-image generative model to visualize brain activity.
- ▪The framework allows MLLMs to leverage visual priors for better clinical-state interpretation.
- ▪Experiments show that GVG enhances EEG understanding and visual generation, outperforming existing text-only alignment methods.
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Computer Science > Artificial Intelligence arXiv:2605.18172 (cs) [Submitted on 18 May 2026] Title:Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs Authors:Junyu Pan, Yansen Wang, Enze Zhang, Baoliang Lu, Weilong Zheng, Dongsheng Li View a PDF of the paper titled Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs, by Junyu Pan and 5 other authors View PDF HTML (experimental) Abstract:Leveraging the universal representations of pre-trained LLMs and MLLMs offers a promising path toward brain foundation models.
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