# Using Deep Learning for Visual Decoding and Reconstruction from Brain Activity: A Review

arXiv: https://arxiv.org/abs/2108.04169 · HTML: https://mlsanity.com/paper/2108.04169/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 9 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision](https://mlsanity.com/paper/1608.03425/) (2016)
- [Reconstructing Faces from fMRI Patterns using Deep Generative Neural Networks](https://mlsanity.com/paper/1810.03856/) (2018)
- [DCNN-GAN: Reconstructing Realistic Image from fMRI](https://mlsanity.com/paper/1901.07368/) (2019)
- [BigGAN-based Bayesian reconstruction of natural images from human brain activity](https://mlsanity.com/paper/2003.06105/) (2020)
- [Reconstructing Perceived Images from Brain Activity by Visually-guided Cognitive Representation and Adversarial Learning](https://mlsanity.com/paper/1906.12181/) (2019)
- [Constraint-free Natural Image Reconstruction from fMRI Signals Based on Convolutional Neural Network](https://mlsanity.com/paper/1801.05151/) (2018)

## 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.