Reconstruction of Visual Stimulus from the EEG Recordings via Generative Adversarial Network

In this work, we address a contemporary research problem in the domain of perceptual brain decoding, involving image synthesis from EEG signals in an adversarial deep learning framework. The specific task involves reconstructing images of different object classes, using the EEG recordings acquired when subjects are shown the images of those objects. For this work, we use an EEG encoder for generating EEG encodings. These EEG encodings act as an input to the generator of the GAN network. In addition to the adversarial loss, we also use perceptual loss for generating decent quality images. Through experiments, we demonstrate that the proposed network is generating better quality images than the available state-of-the-art methods.