# DreamDiffusion: Generating High-Quality Images from Brain EEG Signals

arXiv: https://arxiv.org/abs/2306.16934 · HTML: https://mlsanity.com/paper/2306.16934/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 45 references that are in the map.
- First posted on arXiv 29 Jun 2023.

## Builds on (most cited first, 30 of 32)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Cascaded Diffusion Models for High Fidelity Image Generation](https://mlsanity.com/paper/2106.15282/) (2021)
- [SimMIM: A Simple Framework for Masked Image Modeling](https://mlsanity.com/paper/2111.09886/) (2021)
- [Unsupervised Visual Representation Learning by Context Prediction](https://mlsanity.com/paper/1505.05192/) (2015)
- [Score-based Generative Modeling in Latent Space](https://mlsanity.com/paper/2106.05931/) (2021)
- [CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers](https://mlsanity.com/paper/2204.14217/) (2022)
- [On Fast Sampling of Diffusion Probabilistic Models](https://mlsanity.com/paper/2106.00132/) (2021)
- [Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decoding](https://mlsanity.com/paper/2211.06956/) (2022)
- [Noise Estimation for Generative Diffusion Models](https://mlsanity.com/paper/2104.02600/) (2021)
- [MOABB: Trustworthy algorithm benchmarking for BCIs](https://mlsanity.com/paper/1805.06427/) (2018)
- [Reconstruction of Perceived Images from fMRI Patterns and Semantic Brain Exploration using Instance-Conditioned GANs](https://mlsanity.com/paper/2202.12692/) (2022)

## Built on by (most cited first, 30 of 56)

- [Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation](https://mlsanity.com/paper/2410.13848/) (2024)
- [Controllable Generation with Text-to-Image Diffusion Models: A Survey](https://mlsanity.com/paper/2403.04279/) (2024)
- [BrainVis: Exploring the Bridge between Brain and Visual Signals via Image Reconstruction](https://mlsanity.com/paper/2312.14871/) (2023)
- [ViT-Lens: Towards Omni-modal Representations](https://mlsanity.com/paper/2311.16081/) (2023)
- [Bridging the Vision-Brain Gap with an Uncertainty-Aware Blur Prior](https://mlsanity.com/paper/2503.04207/) (2025)
- [Neuro-3D: Towards 3D Visual Decoding from EEG Signals](https://mlsanity.com/paper/2411.12248/) (2024)
- [Visual Neural Decoding via Improved Visual-EEG Semantic Consistency](https://mlsanity.com/paper/2408.06788/) (2024)
- [ClassDiffusion: More Aligned Personalization Tuning with Explicit Class Guidance](https://mlsanity.com/paper/2405.17532/) (2024)
- [Thought2Text: Text Generation from EEG Signal using Large Language Models (LLMs)](https://mlsanity.com/paper/2410.07507/) (2024)
- [Generative AI Enables EEG Super-Resolution via Spatio-Temporal Adaptive Diffusion Learning](https://mlsanity.com/paper/2407.03089/) (2024)
- [Seeing through the Brain: Image Reconstruction of Visual Perception from Human Brain Signals](https://mlsanity.com/paper/2308.02510/) (2023)
- [Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy](https://mlsanity.com/paper/2401.00430/) (2023)
- [Enhancing EEG-to-Text Decoding through Transferable Representations from Pre-trained Contrastive EEG-Text Masked Autoencoder](https://mlsanity.com/paper/2402.17433/) (2024)
- [NeuSpeech: Decode Neural signal as Speech](https://mlsanity.com/paper/2403.01748/) (2024)
- [Guess What I Think: Streamlined EEG-to-Image Generation with Latent Diffusion Models](https://mlsanity.com/paper/2410.02780/) (2024)
- [CineBrain: A Large-Scale Multi-Modal Brain Dataset During Naturalistic Audiovisual Narrative Processing](https://mlsanity.com/paper/2503.06940/) (2025)
- [Decoding visual brain representations from electroencephalography through Knowledge Distillation and latent diffusion models](https://mlsanity.com/paper/2309.07149/) (2023)
- [TMPQ-DM: Joint Timestep Reduction and Quantization Precision Selection for Efficient Diffusion Models](https://mlsanity.com/paper/2404.09532/) (2024)
- [NECOMIMI: Neural-Cognitive Multimodal EEG-informed Image Generation with Diffusion Models](https://mlsanity.com/paper/2410.00712/) (2024)
- [Towards Neural Foundation Models for Vision: Aligning EEG, MEG, and fMRI Representations for Decoding, Encoding, and Modality Conversion](https://mlsanity.com/paper/2411.09723/) (2024)
- [Naturalistic Music Decoding from EEG Data via Latent Diffusion Models](https://mlsanity.com/paper/2405.09062/) (2024)
- [MoTime: A Dataset Suite for Multimodal Time Series Forecasting](https://mlsanity.com/paper/2505.15072/) (2025)
- [Let EEG Models Learn EEG](https://mlsanity.com/paper/2605.21280/) (2026)
- [You Only Acquire Sparse-channel (YOAS): A Unified Framework for Dense-channel EEG Generation](https://mlsanity.com/paper/2406.15269/) (2024)
- [ViEEG: Hierarchical Visual Neural Representation for EEG Brain Decoding](https://mlsanity.com/paper/2505.12408/) (2025)
- [Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject Emotion Recognition](https://mlsanity.com/paper/2405.19373/) (2024)
- [BrainDreamer: Reasoning-Coherent and Controllable Image Generation from EEG Brain Signals via Language Guidance](https://mlsanity.com/paper/2409.14021/) (2024)
- [Foundation Models for Cross-Domain EEG Analysis Application: A Survey](https://mlsanity.com/paper/2508.15716/) (2025)
- [Subject-Aware Multi-Granularity Alignment for Zero-Shot EEG-to-Image Retrieval](https://mlsanity.com/paper/2604.17782/) (2026)
- [A Survey of Spatio-Temporal EEG data Analysis: from Models to Applications](https://mlsanity.com/paper/2410.08224/) (2024)

## Abstract (arXiv)

This paper introduces DreamDiffusion, a novel method for generating high-quality images directly from brain electroencephalogram (EEG) signals, without the need to translate thoughts into text. DreamDiffusion leverages pre-trained text-to-image models and employs temporal masked signal modeling to pre-train the EEG encoder for effective and robust EEG representations. Additionally, the method further leverages the CLIP image encoder to provide extra supervision to better align EEG, text, and image embeddings with limited EEG-image pairs. Overall, the proposed method overcomes the challenges of using EEG signals for image generation, such as noise, limited information, and individual differences, and achieves promising results. Quantitative and qualitative results demonstrate the effectiveness of the proposed method as a significant step towards portable and low-cost ``thoughts-to-image'', with potential applications in neuroscience and computer vision. The code is available here urlhttps://github.com/bbaaii/DreamDiffusion.