Image-to-Brain Signal Generation for Visual Prosthesis with CLIP Guided Multimodal Diffusion Models

• URL: http://arxiv.org/abs/2509.00787v3
• Date: Sun, 21 Sep 2025 00:49:10 GMT
• Title: Image-to-Brain Signal Generation for Visual Prosthesis with CLIP Guided Multimodal Diffusion Models
• Authors: Ganxi Xu, Jinyi Long, Jia Zhang,
• Abstract summary: We present the first image-to-brain signal framework that generates M/EEG from images.<n>The proposed framework comprises two key components: a pretrained CLIP visual encoder and a cross-attention enhanced U-Net diffusion model.<n>Unlike conventional generative models that rely on simple concatenation for conditioning, our cross-attention modules capture the complex interplay between visual features and brain signal representations.
• Score: 6.761875482596085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual prostheses hold great promise for restoring vision in blind individuals. While researchers have successfully utilized M/EEG signals to evoke visual perceptions during the brain decoding stage of visual prostheses, the complementary process of converting images into M/EEG signals in the brain encoding stage remains largely unexplored, hindering the formation of a complete functional pipeline. In this work, we present, to our knowledge, the first image-to-brain signal framework that generates M/EEG from images by leveraging denoising diffusion probabilistic models enhanced with cross-attention mechanisms. Specifically, the proposed framework comprises two key components: a pretrained CLIP visual encoder that extracts rich semantic representations from input images, and a cross-attention enhanced U-Net diffusion model that reconstructs brain signals through iterative denoising. Unlike conventional generative models that rely on simple concatenation for conditioning, our cross-attention modules capture the complex interplay between visual features and brain signal representations, enabling fine-grained alignment during generation. We evaluate the framework on two multimodal benchmark datasets and demonstrate that it generates biologically plausible brain signals. We also present visualizations of M/EEG topographies across all subjects in both datasets, providing intuitive demonstrations of intra-subject and inter-subject variations in brain signals.

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