See Through Their Minds: Learning Transferable Neural Representation from Cross-Subject fMRI

arXiv:2403.06361v1 Announce Type: new
Abstract: Deciphering visual content from functional Magnetic Resonance Imaging (fMRI) helps illuminate the human vision system. However, the scarcity of fMRI data and noise hamper brain decoding model performance. Previous approaches primarily employ subject-specific models, sensitive to training sample size. In this paper, we explore a straightforward but overlooked solution to address data scarcity. We propose shallow subject-specific adapters to map cross-subject fMRI data into unified representations. Subsequently, a shared deeper decoding model decodes cross-subject features …

arxiv cs.cv cs.hc fmri representation through type