# Comprehensive Review of EEG-to-Output Research: Decoding Neural Signals into Images, Videos, and Audio

arXiv: https://arxiv.org/abs/2412.19999 · HTML: https://mlsanity.com/paper/2412.19999/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 60 references that are in the map.
- First posted on arXiv 28 Dec 2024.

## Builds on (most cited first, 16 of 16)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis](https://mlsanity.com/paper/1903.05628/) (2019)
- [Cinematic Mindscapes: High-quality Video Reconstruction from Brain Activity](https://mlsanity.com/paper/2305.11675/) (2023)
- [Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision](https://mlsanity.com/paper/1608.03425/) (2016)
- [EEG2IMAGE: Image Reconstruction from EEG Brain Signals](https://mlsanity.com/paper/2302.10121/) (2023)
- [A Penny for Your (visual) Thoughts: Self-Supervised Reconstruction of Natural Movies from Brain Activity](https://mlsanity.com/paper/2206.03544/) (2022)
- [Large Transformers are Better EEG Learners](https://mlsanity.com/paper/2308.11654/) (2023)
- [Decoding visual brain representations from electroencephalography through Knowledge Distillation and latent diffusion models](https://mlsanity.com/paper/2309.07149/) (2023)
- [NeuroCine: Decoding Vivid Video Sequences from Human Brain Activties](https://mlsanity.com/paper/2402.01590/) (2024)
- [Generating Novel Scene Compositions from Single Images and Videos](https://mlsanity.com/paper/2103.13389/) (2021)
- [Generative AI in Writing Research Papers: A New Type of Algorithmic Bias and Uncertainty in Scholarly Work](https://mlsanity.com/paper/2312.10057/) (2023)
- [SWIM: Short-Window CNN Integrated with Mamba for EEG-Based Auditory Spatial Attention Decoding](https://mlsanity.com/paper/2409.19884/) (2024)
- [Alljoined1 -- A dataset for EEG-to-Image decoding](https://mlsanity.com/paper/2404.05553/) (2024)

## Built on by (most cited first, 3 of 3)

- [NeuroChat: A Neuroadaptive AI Chatbot for Customizing Learning Experiences](https://mlsanity.com/paper/2503.07599/) (2025)
- [A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyond](https://mlsanity.com/paper/2502.12048/) (2025)
- [NeuroSkill(tm): Proactive Real-Time Agentic System Capable of Modeling Human State of Mind](https://mlsanity.com/paper/2603.03212/) (2026)

## Abstract (arXiv)

Electroencephalography (EEG) is an invaluable tool in neuroscience, offering insights into brain activity with high temporal resolution. Recent advancements in machine learning and generative modeling have catalyzed the application of EEG in reconstructing perceptual experiences, including images, videos, and audio. This paper systematically reviews EEG-to-output research, focusing on state-of-the-art generative methods, evaluation metrics, and data challenges. Using PRISMA guidelines, we analyze 1800 studies and identify key trends, challenges, and opportunities in the field. The findings emphasize the potential of advanced models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers, while highlighting the pressing need for standardized datasets and cross-subject generalization. A roadmap for future research is proposed that aims to improve decoding accuracy and broadening real-world applications.