Human fMRI signals exhibit a spatial patterning that contains detailed information about a
person's mental states. Using classifiers it is possible to access this information and study …

We present MindEye, a novel fMRI-to-image approach to retrieve and reconstruct viewed
images from brain activity. Our model comprises two parallel submodules that are …

With the advent of brain imaging techniques and machine learning tools, much effort has
been devoted to building computational models to capture the encoding of visual …

The mental contents of perception and imagery are thought to be encoded in hierarchical
representations in the brain, but previous attempts to visualize perceptual contents have …

We explore a method for reconstructing visual stimuli from brain activity. Using large
databases of natural images we trained a deep convolutional generative adversarial …

Goal-driven and feedforward-only convolutional neural networks (CNN) have been shown to
be able to predict and decode cortical responses to natural images or videos. Here, we …

Reconstructing perceived natural images from fMRI signals is one of the most engaging
topics of neural decoding research. Prior studies had success in reconstructing either the …

Brain signal visualization has emerged as an active research area, serving as a critical
interface between the human visual system and computer vision models. Diffusion-based …

Reconstructing perceived stimulus (image) only from human brain activity measured with
functional Magnetic Resonance Imaging (fMRI) is a significant task in brain decoding …

Abstract Representations learned by deep convolutional neural networks (CNNs) for object
recognition are a widely investigated model of the processing hierarchy in the human visual …