Previous brain decoding research primarily involves single-subject studies,
reconstructing stimuli via fMRI activity from the same subject. Our study aims
to introduce a generalization technique for cross-subject brain decoding,
facilitated by exploring data alignment methods. We utilized the NSD dataset, a
comprehensive 7T fMRI vision experiment involving multiple subjects exposed to
9841 images, 982 of which were viewed by all. Our approach involved training a
decoding model on one subject, aligning others' data to this space, and testing
the decoding on the second subject. We compared ridge regression, hyper
alignment, and anatomical alignment techniques for fMRI data alignment. We
established that cross-subject brain decoding is feasible, even using around
10% of the total data, or 982 common images, with comparable performance to
single-subject decoding. Ridge regression was the best method for functional
alignment. Through subject alignment, we achieved superior brain decoding and a
potential 90% reduction in scan time. This could pave the way for more
efficient experiments and further advancements in the field, typically
requiring an exorbitant 20-hour scan time per subject.

通过数据对齐方法，我们的研究旨在引入一种针对跨个体大脑解码的泛化技术。我们使用了 NSD 数据集，利用多个受试者的数据对齐，对跨个体大脑解码进行了训练和测试，证实了即使使用约 10% 的数据或 982 个共同图像，交叉个体的大脑解码也是可行的，与单个个体解码相比性能相当。岭回归是最佳的功能对齐方法，通过受试者对齐，我们实现了更优秀的大脑解码以及可能的扫描时间缩减 90%，为更高效的实验和领域的进一步突破铺平了道路，通常需要耗费昂贵的每个受试者的 20 小时扫描时间。