Decoding Realistic Images from Brain Activity with Contrastive Self-supervision and Latent Diffusion

TL;DR

This paper introduces a two-phase framework combining contrastive self-supervised learning and latent diffusion models to reconstruct realistic images from fMRI brain activity, advancing brain-computer interface capabilities.

Contribution

It presents a novel two-phase approach that effectively decodes visual stimuli from brain signals and interprets the connection between diffusion models and the human visual system.

Findings

Reconstructs highly plausible images from fMRI data.

Provides quantitative insights into the link between diffusion models and brain visual processing.

Outperforms existing methods on challenging benchmarks.

Abstract

Reconstructing visual stimuli from human brain activities provides a promising opportunity to advance our understanding of the brain's visual system and its connection with computer vision models. Although deep generative models have been employed for this task, the challenge of generating high-quality images with accurate semantics persists due to the intricate underlying representations of brain signals and the limited availability of parallel data. In this paper, we propose a two-phase framework named Contrast and Diffuse (CnD) to decode realistic images from functional magnetic resonance imaging (fMRI) recordings. In the first phase, we acquire representations of fMRI data through self-supervised contrastive learning. In the second phase, the encoded fMRI representations condition the diffusion model to reconstruct visual stimulus through our proposed concept-aware conditioning method. Experimental results show that CnD reconstructs highly plausible images on challenging benchmarks. We also provide a quantitative interpretation of the connection between the latent diffusion model (LDM) components and the human brain's visual system. In summary, we present an effective approach for reconstructing visual stimuli based on human brain activity and offer a novel framework to understand the relationship between the diffusion model and the human brain visual system.