Using Deep Learning for Visual Decoding and Reconstruction from Brain Activity: A Review
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Abstract (arXiv)
This literature review will discuss the use of deep learning methods for image reconstruction using fMRI data. More specifically, the quality of image reconstruction will be determined by the choice in decoding and reconstruction architectures. I will show that these structures can struggle with adaptability to various input stimuli due to complicated objects in images. Also, the significance of feature representation will be evaluated. This paper will conclude the use of deep learning within visual decoding and reconstruction is highly optimal when using variations of deep neural networks and will provide details of potential future work.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 25 references that are in the map. First posted on arXiv 9 Aug 2021.
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6 of 25 references.
References · 6
- 2016Neural Encoding and Decoding with Deep Learning for Dynamic Natural Visioncited by 58
- 2018Reconstructing Faces from fMRI Patterns using Deep Generative Neural Networkscited by 43
- 2019DCNN-GAN: Reconstructing Realistic Image from fMRIcited by 16
- 2020BigGAN-based Bayesian reconstruction of natural images from human brain activitycited by 10
- 2019Reconstructing Perceived Images from Brain Activity by Visually-guided Cognitive Representation and Adversarial Learningcited by 5
- 2018Constraint-free Natural Image Reconstruction from fMRI Signals Based on Convolutional Neural Networkcited by 4
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