See Through Their Minds: Learning Transferable Brain Decoding Models from Cross-Subject fMRI

Deciphering visual content from fMRI sheds light on the human vision system, but data scarcity and noise limit brain decoding model performance. Traditional approaches rely on subject-specific models, which are sensitive to training sample size. In this paper, we address data scarcity by proposing shallow subject-specific adapters to map cross-subject fMRI data into unified representations. A shared deep decoding model then decodes these features into the target feature space. We use both visual and textual supervision for multi-modal brain decoding and integrate high-level perception decoding with pixel-wise reconstruction guided by high-level perceptions. Our extensive experiments reveal several interesting insights: 1) Training with cross-subject fMRI benefits both high-level and low-level decoding models; 2) Merging high-level and low-level information improves reconstruction performance at both levels; 3) Transfer learning is effective for new subjects with limited training data by training new adapters; 4) Decoders trained on visually-elicited brain activity can generalize to decode imagery-induced activity, though with reduced performance.