脑电信号理解一直受限于文本对齐的信息损失，GVG用生成图像作为桥梁，让MLLM能利用视觉先验进行更丰富的解读。做脑机接口或神经科学AI的团队，这个框架值得关注，轻量模型就能达到大模型效果。
现有方法将脑电信号（EEG）与文本对齐，但会丢失精细的感知信息。研究者提出生成式视觉对齐（GVG）框架，通过EEG到图像的生成模型作为视觉翻译器，为非视觉EEG生成实例特定的代理图像，让多模态大模型（MLLM）利用视觉先验进行临床状态解读。在GVG-X-Omni和GVG-Janus两个骨干模型上验证，仅图像对齐的轻量模型在冻结7B参数骨干上仅调优170M参数，即可匹配1.7B参数文本对齐基线。进一步的三模态图像+文本对齐实验显示，视觉代理对齐能有效补充文本对齐，在EEG理解和视觉生成上均有一致提升。
Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs
Leveraging the universal representations of pre-trained LLMs and MLLMs offers a promising path toward brain foundation models. However, visually-evoked EEG datasets remain scarce, leading existing methods to align neural signals mainly with abstract text, a lossy translation that may discard fine-grained perceptual information encoded in brain activity. We propose Generative Visual Grounding (GVG), a framework that visualizes the invisible by using an EEG-to-image generative model as a visual translator. Instead of forcing EEG into text alone, GVG hallucinates instance-specific proxy images for non-visual EEG, providing structured visual contexts that allow MLLMs to exploit their visual priors for clinical-state interpretation. We validate this idea on two MLLM backbones, GVG-X-Omni and GVG-Janus. Image-only alignment is already competitive: the lightweight GVG-X-Omni matches 1.7B-parameter text-aligned baselines while tuning only 170M parameters on a frozen 7B backbone. We further extend GVG-Janus with trimodal Image+Text alignment, where text supplies categorical semantic anchors and visual proxies enrich neural representations with perceptual details. Experiments show consistent gains in EEG understanding and visual generation, suggesting visual proxy grounding as an effective complement to textual alignment.