A multimodal pipeline decodes EEG into 3D meshes via EEG-to-image, MLLM reasoning, diffusion, and single-image-to-3D conversion, reporting 85.4% 10-way accuracy and 0.648 CLIPScore.
In: European Conference on Computer Vision
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EEG2Vision reconstructs images from EEG using diffusion models plus LLM-guided boosting, with reconstruction quality holding up reasonably as electrode count drops from 128 to 24 channels.
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Brain3D: EEG-to-3D Decoding of Visual Representations via Multimodal Reasoning
A multimodal pipeline decodes EEG into 3D meshes via EEG-to-image, MLLM reasoning, diffusion, and single-image-to-3D conversion, reporting 85.4% 10-way accuracy and 0.648 CLIPScore.
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EEG2Vision: A Multimodal EEG-Based Framework for 2D Visual Reconstruction in Cognitive Neuroscience
EEG2Vision reconstructs images from EEG using diffusion models plus LLM-guided boosting, with reconstruction quality holding up reasonably as electrode count drops from 128 to 24 channels.