突破个体差异限制，实现跨被试的通用视觉脑解码。
Toward Generalizing Visual Brain Decoding to Unseen Subjects
- 统一模型处理所有被试，避免为每人定制网络结构。
- 训练被试越多，跨被试解码效果越好，177名被试验证有效。
- 不同神经网络架构均具备泛化能力，适合大规模脑数据研究。
视觉脑解码旨在从人脑活动信号中还原视觉信息。尽管进展显著，当前研究仍面临无法泛化到未见被试的核心瓶颈。以往方法通常针对个体建模，而本研究首次系统探索脑解码在未见被试上的可行性。我们整合了来自人类连接组计划（HCP）电影观看任务的177名被试的图像-fMRI配对数据集，支持大规模跨被试分析。提出一种统一处理范式，不为每位被试设置独立网络头或分词器，可扩展至大量被试。实验表明：随着训练被试数量增加，模型泛化能力显著提升；多种主流架构（MLP、CNN、Transformer）均展现泛化潜力；解码性能受被试间脑活动相似性影响。结果揭示个体脑活动存在内在共性，未来有望基于更大规模数据训练脑解码基础模型。
原文摘要 · Abstract (English)
Visual brain decoding aims to decode visual information from human brain activities. Despite the great progress, one critical limitation of current brain decoding research lies in the lack of generalization capability to unseen subjects. Prior works typically focus on decoding brain activity of individuals based on the observation that different subjects exhibit different brain activities, while it remains unclear whether brain decoding can be generalized to unseen subjects. This study aims to answer this question. We first consolidate an image-fMRI dataset consisting of stimulus-image and fMRI-response pairs, involving 177 subjects in the movie-viewing task of the Human Connectome Project (HCP). This dataset allows us to investigate the brain decoding performance with the increase of participants. We then present a learning paradigm that applies uniform processing across all subjects, instead of employing different network heads or tokenizers for individuals as in previous methods, which can accommodate a large number of subjects to explore the generalization capability across different subjects. A series of experiments are conducted and we have the following findings. First, the network exhibits clear generalization capabilities with the increase of training subjects. Second, the generalization capability is common to popular network architectures (MLP, CNN and Transformer). Third, the generalization performance is affected by the similarity between subjects. Our findings reveal the inherent similarities in brain activities across individuals. With the emerging of larger and more comprehensive datasets, it is possible to train a brain decoding foundation model in the future. Codes and models can be found at https://github.com/Xiangtaokong/TGBD.
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