fMRI Representation

fMRI representation learning aims to extract meaningful information from functional magnetic resonance imaging data, enabling better understanding of brain activity and facilitating applications like brain-computer interfaces and disease diagnosis. Current research focuses on developing sophisticated deep learning models, including transformers, graph neural networks, and autoencoders, often employing self-supervised learning techniques to overcome data scarcity and noise inherent in fMRI signals. These advancements are improving the accuracy of image reconstruction from brain activity, decoding semantic information, and identifying neurological disorders, ultimately bridging the gap between computer vision and neuroscience.