Hyperrealistic neural decoding: Linear reconstruction of face stimuli from fMRI measurements via the GAN latent space


We introduce a new framework for hyperrealistic reconstruction of perceived naturalistic stimuli from brain recordings. To this end, we embrace the use of generative adversarial networks (GANs) at the earliest step of our neural decoding pipeline by acquiring functional magnetic resonance imaging data as subjects perceived face images created by the generator network of a GAN. Subsequently, we used a linear decoding approach to predict the latent state of the GAN from brain data. Hence, latent representations that are needed for stimulus (re-)generation are obtained, leading to ground-breaking image reconstructions. Altogether, we have developed a highly promising approach for decoding neural representations of real-world data, which may pave the way for systematically analyzing neural information processing in the functional brain.