Constraint-free Natural Image Reconstruction from fMRI Signals Based on Convolutional Neural Network

TL;DR

This paper introduces a novel CNN-based method for reconstructing natural images from fMRI signals without using semantic priors, achieving promising results in position and shape accuracy.

Contribution

It presents a constraint-free approach that maps fMRI signals to CNN features and iteratively reconstructs natural images without prior semantic information.

Findings

Reconstructed images resemble natural stimuli in position and shape

Hierarchical CNN features effectively model human visual perception

Method works across various image categories without training on specific classes

Abstract

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.