Deep Natural Image Reconstruction from Human Brain Activity Based on Conditional Progressively Growing Generative Adversarial Networks  

作　　者：Wei Huang Hongmei Yan Chong Wang Xiaoqing Yang Jiyi Li Zhentao Zuo Jiang Zhang Huafu Chen 

机构地区：[1]The MOE Key Lab for Neuroinformation,University of Electronic Science and Technology of China,Chengdu 610054,China [2]State Key Laboratory of Brain and Cognitive Science,Beijing MR Center for Brain Research,Institute of Biophysics,Chinese Academy of Sciences,Beijing 100101,China [3]Department of Medical Information Engineering,Sichuan University,Chengdu 610065,China

出　　处：《Neuroscience Bulletin》2021年第3期369-379,共11页神经科学通报（英文版）

基　　金：supported by the National Natural Science Foundation of China(61773094,61533006,U1808204,31730039,31671133,and 61876114);the Ministry of Science and Technology of China(2015CB351701);the National Major Scientific Instruments and Equipment Development Project(ZDYZ2015-2);a Chinese Academy of Sciences Strategic Priority Research Program B grant(XDB32010300)。

摘　　要：Brain decoding based on functional magnetic resonance imaging has recently enabled the identification of visual perception and mental states.However,due to the limitations of sample size and the lack of an effective reconstruction model,accurate reconstruction of natural images is still a major challenge.The current,rapid development of deep learning models provides the possibility of overcoming these obstacles.Here,we propose a deep learning-based framework that includes a latent feature extractor,a latent feature decoder,and a natural image generator,to achieve the accurate reconstruction of natural images from brain activity.The latent feature extractor is used to extract the latent features of natural images.The latent feature decoder predicts the latent features of natural images based on the response signals from the higher visual cortex.The natural image generatoris applied to generate reconstructed images from the predicted latent features of natural images and the response signals from the visual cortex.Quantitative and qualitative evaluations were conducted with test images.The results showed that the reconstructed image achieved comparable,accurate reproduction of the presented image in both highlevel semantic category information and low-level pixel information.The framework we propose shows promise for decoding the brain activity.

关 键 词：Brain decoding FMRI Deep learning 

分 类 号：R338[医药卫生—人体生理学]