Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning
  and Diffusion Priors
Authors: name: Paul S. Scotti, name: Atmadeep Banerjee, name: Jimmie Goode, name: Stepan Shabalin, name: Alex Nguyen, name: Ethan Cohen, name: Aidan J. Dempster, name: Nathalie Verlinde, name: Elad Yundler, name: David Weisberg, name: Kenneth A. Norman, name: Tanishq Mathew Abraham

We present MindEye, a novel fMRI-to-image approach to retrieve and
reconstruct viewed images from brain activity. Our model comprises two parallel
submodules that are specialized for retrieval (using contrastive learning) and
reconstruction (using a diffusion prior). MindEye can map fMRI brain activity
to any high dimensional multimodal latent space, like CLIP image space,
enabling image reconstruction using generative models that accept embeddings
from this latent space. We comprehensively compare our approach with other
existing methods, using both qualitative side-by-side comparisons and
quantitative evaluations, and show that MindEye achieves state-of-the-art
performance in both reconstruction and retrieval tasks. In particular, MindEye
can retrieve the exact original image even among highly similar candidates
indicating that its brain embeddings retain fine-grained image-specific
information. This allows us to accurately retrieve images even from large-scale
databases like LAION-5B. We demonstrate through ablations that MindEye's
performance improvements over previous methods result from specialized
submodules for retrieval and reconstruction, improved training techniques, and
training models with orders of magnitude more parameters. Furthermore, we show
that MindEye can better preserve low-level image features in the
reconstructions by using img2img, with outputs from a separate autoencoder. All
code is available on GitHub.