用脑扫描图重建图像，为脑机接口提供新思路
A Survey of fMRI to Image Reconstruction
- 提出fMRI-to-Image学习框架，系统梳理重建方法
- 指出数据少、个体差异大、语义不一致等核心难题
- 适合神经科学与脑机接口研究者参考
基于功能磁共振成像（fMRI）的图像重建在解码人类感知中起关键作用，广泛应用于神经科学与脑机接口领域。尽管深度学习和大规模数据集推动了进展，仍面临数据稀缺、跨被试差异大、语义一致性低等挑战。为此，本文提出fMRI-to-Image学习概念，并首次对该领域进行系统性综述。综述归纳了关键技术路径，包括fMRI信号编码、特征映射与图像生成器设计，同时指出了未来有潜力的研究方向，为该新兴领域的后续研究提供重要参考。
原文摘要 · Abstract (English)
Functional magnetic resonance imaging (fMRI) based image reconstruction plays a pivotal role in decoding human perception, with applications in neuroscience and brain-computer interfaces. While recent advancements in deep learning and large-scale datasets have driven progress, challenges such as data scarcity, cross-subject variability, and low semantic consistency persist. To address these issues, we introduce the concept of fMRI-to-Image Learning (fMRI2Image) and present the first systematic review in this field. This review highlights key challenges, categorizes methodologies such as fMRI signal encoding, feature mapping, and image generator. Finally, promising research directions are proposed to advance this emerging frontier, providing a reference for future studies.
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