  In this study, we tackle a modern research challenge within the field of
perceptual brain decoding, which revolves around synthesizing images from EEG
signals using an adversarial deep learning framework. The specific objective is
to recreate images belonging to various object categories by leveraging EEG
recordings obtained while subjects view those images. To achieve this, we
employ a Transformer-encoder based EEG encoder to produce EEG encodings, which
serve as inputs to the generator component of the GAN network. Alongside the
adversarial loss, we also incorporate perceptual loss to enhance the quality of
the generated images.
