Chong Wang

TL;DR: This work developed a novel fMRI-conditional video generative adversarial network (f-CVGAN) to reconstruct rapid video stimuli from evoked fMRI responses and suggests that slow blood oxygen level-dependent signals describe neural representations of the fast perceptual process that can be decoded in practice.

TL;DR: A decoding model based on long short‐term memory (LSTM) network to decode five object categories from multitime response signals evoked by natural images is proposed and it is shown that the higher visual cortex contains more semantic category information needed for visual perceptual decoding than lower VC.

TL;DR: An end-to-end perception reconstruction model called the similarity-conditions generative adversarial network (SC-GAN), where visually perceptible images are reconstructed based on human visual cortex responses, which offers a potential perspective for decoding the brain activity of complex natural stimuli.

TL;DR: Results showed decreased phase lag within the core and extended face areas (face processing system) and increased phase lag between the face processing system and frontoparietal network, indicating a reorganization of inter-regional relationships of the two systems.