Brain2Image

Abstract

Reading the human mind has been a hot topic in the last decades, and recent research in neuroscience has found evidence on the possibility of decoding, from neuroimaging data, how the human brain works. At the same time, the recent rediscovery of deep learning combined to the large interest of scientific community on generative methods has enabled the generation of realistic images by learning a data distribution from noise. The quality of generated images increases when the input data conveys information on visual content of images. Leveraging on these recent trends, in this paper we present an approach for generating images using visually-evoked brain signals recorded through an electroencephalograph (EEG). More specifically, we recorded EEG data from several subjects while observing images on a screen and tried to regenerate the seen images. To achieve this goal, we developed a deep-learning framework consisting of an LSTM stacked with a generative method, which learns a more compact and noise-free representation of EEG data and employs it to generate the visual stimuli evoking specific brain responses. OurBrain2Image approach was trained and tested using EEG data from six subjects while they were looking at images from 40 ImageNet classes. As generative models, we compared variational autoencoders (VAE) and generative adversarial networks (GAN). The results show that, indeed, our approach is able to generate an image drawn from the same distribution of the shown images. Furthermore, GAN, despite generating less realistic images, show better performance than VAE, especially as concern sharpness. The obtained performance provides useful hints on the fact that EEG contains patterns related to visual content and that such patterns can be used to effectively generate images that are semantically coherent to the evoking visual stimuli.