  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.
