Mental image reconstruction from human brain activity

Abstract

Visual images perceived by humans can be reconstructed from their brain activity. However, the visualization (externalization) of mental imagery remains a challenge. In this study, we demonstrated that the visual image reconstruction method proposed in the seminal study by Shen et al. (2019) heavily relied on low-level visual information decoded from the brain and could not efficiently utilize semantic information that would be recruited during mental imagery. To address this limitation, we extended the previous method to a Bayesian estimation framework and introduced the assistance of semantic information into it. Our proposed framework successfully reconstructed both seen (i.e., directly captured by the human eye) and imagined images from the brain activity. These results suggest that our framework would provide a technology for directly investigating the subjective contents of the brain.