# Generating Visual Stimuli from EEG Recordings using Transformer-encoder based EEG encoder and GAN

arXiv: https://arxiv.org/abs/2402.10115 · HTML: https://mlsanity.com/paper/2402.10115/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 15 Feb 2024.

## Builds on (most cited first, 5 of 5)

- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Conditional Image Synthesis With Auxiliary Classifier GANs](https://mlsanity.com/paper/1610.09585/) (2016)
- [Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks](https://mlsanity.com/paper/1511.06448/) (2015)
- [An EEG-based Image Annotation System](https://mlsanity.com/paper/1711.02383/) (2017)
- [Universal EEG Encoder for Learning Diverse Intelligent Tasks](https://mlsanity.com/paper/1911.12152/) (2019)

## Abstract (arXiv)

In this study, we tackle a modern research challenge within the field of perceptual brain decoding, which revolves around synthesizing images from EEG signals using an adversarial deep learning framework. The specific objective is to recreate images belonging to various object categories by leveraging EEG recordings obtained while subjects view those images. To achieve this, we employ a Transformer-encoder based EEG encoder to produce EEG encodings, which serve as inputs to the generator component of the GAN network. Alongside the adversarial loss, we also incorporate perceptual loss to enhance the quality of the generated images.