fMRI to Text

fMRI-to-text research aims to decode the content of a person's thoughts or visual imagery directly from their brain activity measured via fMRI. Current approaches leverage deep learning models, often incorporating techniques like encoder-decoder networks and alignment strategies to map fMRI signals to textual descriptions or visual representations, sometimes using pre-trained models like CLIP as a bridge between modalities. This field is advancing cross-subject decoding capabilities and developing more efficient models, improving the accuracy and reducing the computational demands of brain decoding. The ultimate goal is to enhance our understanding of brain function and potentially enable more sophisticated brain-computer interfaces.