# EEG2IMAGE: Image Reconstruction from EEG Brain Signals

arXiv: https://arxiv.org/abs/2302.10121 · HTML: https://mlsanity.com/paper/2302.10121/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2023.

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

- [BrainVis: Exploring the Bridge between Brain and Visual Signals via Image Reconstruction](https://mlsanity.com/paper/2312.14871/) (2023)
- [Learning Robust Deep Visual Representations from EEG Brain Recordings](https://mlsanity.com/paper/2310.16532/) (2023)
- [Neuro-3D: Towards 3D Visual Decoding from EEG Signals](https://mlsanity.com/paper/2411.12248/) (2024)
- [Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy](https://mlsanity.com/paper/2401.00430/) (2023)
- [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)
- [BELT-2: Bootstrapping EEG-to-Language representation alignment for multi-task brain decoding](https://mlsanity.com/paper/2409.00121/) (2024)
- [NECOMIMI: Neural-Cognitive Multimodal EEG-informed Image Generation with Diffusion Models](https://mlsanity.com/paper/2410.00712/) (2024)
- [BrainDreamer: Reasoning-Coherent and Controllable Image Generation from EEG Brain Signals via Language Guidance](https://mlsanity.com/paper/2409.14021/) (2024)
- [AI for Biomedicine in the Era of Large Language Models](https://mlsanity.com/paper/2403.15673/) (2024)
- [A Survey of Spatio-Temporal EEG data Analysis: from Models to Applications](https://mlsanity.com/paper/2410.08224/) (2024)
- [Comprehensive Review of EEG-to-Output Research: Decoding Neural Signals into Images, Videos, and Audio](https://mlsanity.com/paper/2412.19999/) (2024)
- [A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyond](https://mlsanity.com/paper/2502.12048/) (2025)
- [BrainAgent: A Large Language Model-Driven Multi-Agent Framework for Autonomous Brain Signal Understanding](https://mlsanity.com/paper/2606.25400/) (2026)
- [Alljoined1 -- A dataset for EEG-to-Image decoding](https://mlsanity.com/paper/2404.05553/) (2024)
- [EIT-1M: One Million EEG-Image-Text Pairs for Human Visual-textual Recognition and More](https://mlsanity.com/paper/2407.01884/) (2024)
- [From Pixels to Feelings: Aligning MLLMs with Human Cognitive Perception of Images](https://mlsanity.com/paper/2511.22805/) (2025)
- [Neural Brain Fields: A NeRF-Inspired Approach for Generating Nonexistent EEG Electrodes](https://mlsanity.com/paper/2601.00012/) (2025)
- [On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach](https://mlsanity.com/paper/2403.17181/) (2024)
- [BrainDecoder: Style-Based Visual Decoding of EEG Signals](https://mlsanity.com/paper/2409.05279/) (2024)
- [DynaMind: Reconstructing Dynamic Visual Scenes from EEG by Aligning Temporal Dynamics and Multimodal Semantics to Guided Diffusion](https://mlsanity.com/paper/2509.01177/) (2025)
- [EEG2Vision: A Multimodal EEG-Based Framework for 2D Visual Reconstruction in Cognitive Neuroscience](https://mlsanity.com/paper/2604.08063/) (2026)
- [Multi-Level Bidirectional Biomimetic Learning for EEG-Based Visual Decoding](https://mlsanity.com/paper/2605.04680/) (2026)
- [Mapping EEG Signals to Visual Stimuli: A Deep Learning Approach to Match vs. Mismatch Classification](https://mlsanity.com/paper/2309.04153/) (2023)
- [CATVis: Context-Aware Thought Visualization](https://mlsanity.com/paper/2507.11522/) (2025)
- [NeuroSketch: A Practical Design Recipe for Neural Decoding](https://mlsanity.com/paper/2512.09524/) (2025)
- [Brain-Gen: Towards Interpreting Neural Signals for Stimulus Reconstruction Using Transformers and Latent Diffusion Models](https://mlsanity.com/paper/2512.18843/) (2025)
- [Brain3D: EEG-to-3D Decoding of Visual Representations via Multimodal Reasoning](https://mlsanity.com/paper/2604.08068/) (2026)
- [Lost in Visual Translation: A VLM-Assisted Perceptual-Semantic Coherence Framework for EEG-to-Image Reconstruction](https://mlsanity.com/paper/2607.12364/) (2026)

## Abstract (arXiv)

Reconstructing images using brain signals of imagined visuals may provide an augmented vision to the disabled, leading to the advancement of Brain-Computer Interface (BCI) technology. The recent progress in deep learning has boosted the study area of synthesizing images from brain signals using Generative Adversarial Networks (GAN). In this work, we have proposed a framework for synthesizing the images from the brain activity recorded by an electroencephalogram (EEG) using small-size EEG datasets. This brain activity is recorded from the subject's head scalp using EEG when they ask to visualize certain classes of Objects and English characters. We use a contrastive learning method in the proposed framework to extract features from EEG signals and synthesize the images from extracted features using conditional GAN. We modify the loss function to train the GAN, which enables it to synthesize 128x128 images using a small number of images. Further, we conduct ablation studies and experiments to show the effectiveness of our proposed framework over other state-of-the-art methods using the small EEG dataset.