ArticleFrontiers in neuroscience2022
Brain2Pix: Fully convolutional naturalistic video frame reconstruction from brain activity.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Symbiotic brain-machine drawing via visual brain-computer interfaces.npj biomedical innovations · 2026Article
- Article
- RECONSTRUCTING RETINAL VISUAL IMAGES FROM 3T FMRI DATA ENHANCED BY UNSUPERVISED LEARNING.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Brain2GAN: Feature-disentangled neural encoding and decoding of visual perception in the primate brain.PLoS computational biology · 2024Article
- Predicting subjective ratings of affect and comprehensibility with text features: a reader response study of narrative poetry.Frontiers in psychology · 2024Article
- Hyperrealistic neural decoding for reconstructing faces from fMRI activations via the GAN latent space.Scientific reports · 2022Article
- Reconstruction of perceived face images from brain activities based on multi-attribute constraints.Frontiers in neuroscience · 2022Article
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6 authors.
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Abstract
Reconstructing complex and dynamic visual perception from brain activity remains a major challenge in machine learning applications to neuroscience. Here, we present a new method for reconstructing naturalistic images and videos from very large single-participant functional magnetic resonance imaging data that leverages the recent success of image-to-image transformation networks. This is achieved by exploiting spatial information obtained from retinotopic mappings across the visual system. More specifically, we first determine what position each voxel in a particular region of interest would represent in the visual field based on its corresponding receptive field location. Then, the 2D image representation of the brain activity on the visual field is passed to a fully convolutional image-to-image network trained to recover the original stimuli using VGG feature loss with an adversarial regularizer. In our experiments, we show that our method offers a significant improvement over existing video reconstruction techniques.
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