ArticleCommunications biology2019
Reconstructing faces from fMRI patterns using deep generative neural networks.
Article in Communications biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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Who cites it
47 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Natural Image Reconstruction From fMRI Using Deep Learning: A Survey.Frontiers in neuroscience · 2021Pooled it
- Preferred visual experiences provide intuitive descriptions of the functional properties of the cortical navigation network.bioRxiv : the preprint server for biology · 2026Article
- A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning.Scientific data · 2026Article
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- Functional diversity of visual cortex improves constraint-free natural image reconstruction from human brain activity.Fundamental research · 2025Article
- Large-scale foundation models and generative AI for BigData neuroscience.Neuroscience research · 2025Review
- Improved image reconstruction from brain activity through automatic image captioning.Scientific reports · 2025Article
- Population encoding of observed and actual somatosensations in the human posterior parietal cortex.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- An fMRI dataset in response to large-scale short natural dynamic facial expression videos.Scientific data · 2024Article
- Emerging Brain-to-Content Technologies from Generative AI and Deep Representation Learning.IEEE signal processing magazine · 2024Article
- Perceptual Expertise and Attention: An Exploration using Deep Neural Networks.bioRxiv : the preprint server for biology · 2024Article
- The new science of sleep: From cells to large-scale societies.PLoS biology · 2024Article
- Brain2GAN: Feature-disentangled neural encoding and decoding of visual perception in the primate brain.PLoS computational biology · 2024Article
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- Disentangled deep generative models reveal coding principles of the human face processing network.PLoS computational biology · 2024Article
- Downstream network transformations dissociate neural activity from causal functional contributions.Scientific reports · 2024Article
- Through their eyes: Multi-subject brain decoding with simple alignment techniques.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Reconstructing visual illusory experiences from human brain activity.Science advances · 2023Article
- Dual-Guided Brain Diffusion Model: Natural Image Reconstruction from Human Visual Stimulus fMRI.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Although distinct categories are reliably decoded from fMRI brain responses, it has proved more difficult to distinguish visually similar inputs, such as different faces. Here, we apply a recently developed deep learning system to reconstruct face images from human fMRI. We trained a variational auto-encoder (VAE) neural network using a GAN (Generative Adversarial Network) unsupervised procedure over a large data set of celebrity faces. The auto-encoder latent space provides a meaningful, topologically organized 1024-dimensional description of each image. We then presented several thousand faces to human subjects, and learned a simple linear mapping between the multi-voxel fMRI activation patterns and the 1024 latent dimensions. Finally, we applied this mapping to novel test images, translating fMRI patterns into VAE latent codes, and codes into face reconstructions. The system not only performed robust pairwise decoding (>95% correct), but also accurate gender classification, and even decoded which face was imagined, rather than seen.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.