A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyond
cs.AI, cs.HC, cs.LG
Submitted: 2025-02-17
Updated: 2026-09-15
License: http://creativecommons.org/licenses/by/4.0/
The gist: Decoding neural activity into human-interpretable representations is a key research direction in brain-computer interfaces (BCIs) and computational neuroscience.
Terminology
Abstract
Decoding neural activity into human-interpretable representations is a key research direction in brain-computer interfaces (BCIs) and computational neuroscience. Recent progress in machine learning and generative AI has driven growing interest in transforming non-invasive Electroencephalography (EEG) signals into images, text, and audio. This survey consolidates and analyzes developments across EEG-to-image synthesis, EEG-to-text generation, and EEG-to-audio reconstruction. We conducted a structured literature search across major databases (2017-2025), extracting key information on datasets, generative architectures (GANs, VAEs, transformers, diffusion models), EEG feature-encoding techniques, evaluation metrics, and the major challenges shaping current work in this area. Our review finds that EEG-to-image models predominantly employ encoder-decoder architectures built on GANs, VAEs, or diffusion models; EEG-to-text approaches increasingly leverage transformer-based language models for open-vocabulary decoding; and EEG-to-audio methods commonly map EEG signals to mel-spectrograms that are subsequently rendered into audio using neural vocoders. Despite promising advances, the field remains constrained by small and heterogeneous datasets, limited cross-subject generalization, and the absence of standardized benchmarks. By consolidating methodological trends and available datasets, this survey provides a foundational reference for advancing EEG-based generative AI and supporting reproducible research. We further highlight open-source datasets and baseline implementations to facilitate systematic benchmarking and accelerate progress in EEG-driven neural decoding.
Sources
- DreamDiffusion: Generating High-Quality Images from Brain EEG Signals
- DeWave: Discrete EEG Waves Encoding for Brain Dynamics to Text Translation
- Bridging Brain Signals and Language: A Deep Learning Approach to EEG-to-Text Decoding
- Neural Spelling: A Spell-Based BCI System for Language Neural Decoding
- ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotation
- EEG2TEXT-CN: An Exploratory Study of Open-Vocabulary Chinese Text-EEG Alignment via Large Language Model and Contrastive Learning on ChineseEEG
- Toward Fully-End-to-End Listened Speech Decoding from EEG Signals
- ETS: Open Vocabulary Electroencephalography-To-Text Decoding and Sentiment Classification
- Thought2Text: Text Generation from EEG Signal using Large Language Models (LLMs)
- Visual Decoding and Reconstruction via EEG Embeddings with Guided Diffusion
- EEG2TEXT: Open Vocabulary EEG-to-Text Decoding with EEG Pre-Training and Multi-View Transformer
- Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation
- Decoding Natural Images from EEG for Object Recognition
- Naturalistic Music Decoding from EEG Data via Latent Diffusion Models
- SEE: Semantically Aligned EEG-to-Text Translation
- EEG2Mel: Reconstructing Sound from Brain Responses to Music
- Interpretable EEG-to-Image Generation with Semantic Prompts
- BLEURT: Learning Robust Metrics for Text Generation
- Enhancing EEG-to-Text Decoding through Transferable Representations from Pre-trained Contrastive EEG-Text Masked Autoencoder
- BERTScore: Evaluating Text Generation with BERT
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