用统一脑信号空间实现高效多主体图像重建
Efficient Multi Subject Visual Reconstruction from fMRI Using Aligned Representations
- 构建共享脑信号空间，对齐不同受试者脑活动
- 仅需少量数据即可完成高精度图像重建
- 适合跨主体、小样本的脑机接口研究
本文提出一种基于fMRI的视觉图像重建新方法，通过构建跨受试者的通用脑信号表示空间，在训练中对齐各受试者脑信号，形成语义一致的公共脑模型。该方法利用轻量级模块对齐参考受试者，相比传统端到端训练更高效，尤其在低数据条件下表现优异。我们在多个数据集上验证了该方法的有效性，结果表明该公共空间具有受试者和数据集无关性。
原文摘要 · Abstract (English)
This work introduces a novel approach to fMRI-based visual image reconstruction using a subject-agnostic common representation space. We show that the brain signals of the subjects can be aligned in this common space during training to form a semantically aligned common brain. This is leveraged to demonstrate that aligning subject-specific lightweight modules to a reference subject is significantly more efficient than traditional end-to-end training methods. Our approach excels in low-data scenarios. We evaluate our methods on different datasets, demonstrating that the common space is subject and dataset-agnostic.
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