Decoding Neural Representations from Cross-Subject fMRI Data

The author proposes a method, STTM, that leverages cross-subject fMRI data to learn transferable neural representations shared across human brains. By pre-training on the NSD dataset and performing transfer learning on the GOD dataset, the approach achieves comparable or superior decoding performance across various tasks.