  In recent years, research on decoding brain activity based on functional
magnetic resonance imaging (fMRI) has made remarkable achievements. However,
constraint-free natural image reconstruction from brain activity is still a
challenge. The existing methods simplified the problem by using semantic prior
information or just reconstructing simple images such as letters and digitals.
Without semantic prior information, we present a novel method to reconstruct
nature images from fMRI signals of human visual cortex based on the computation
model of convolutional neural network (CNN). Firstly, we extracted the units
output of viewed natural images in each layer of a pre-trained CNN as CNN
features. Secondly, we transformed image reconstruction from fMRI signals into
the problem of CNN feature visualizations by training a sparse linear
regression to map from the fMRI patterns to CNN features. By iteratively
optimization to find the matched image, whose CNN unit features become most
similar to those predicted from the brain activity, we finally achieved the
promising results for the challenging constraint-free natural image
reconstruction. As there was no use of semantic prior information of the
stimuli when training decoding model, any category of images (not constraint by
the training set) could be reconstructed theoretically. We found that the
reconstructed images resembled the natural stimuli, especially in position and
shape. The experimental results suggest that hierarchical visual features can
effectively express the visual perception process of human brain.
