A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyond

TL;DR

This survey reviews recent advances in transforming EEG signals into images, text, and audio using generative AI, highlighting current methods, datasets, challenges, and future directions for brain-computer interface research.

Contribution

It consolidates and analyzes developments in EEG-to-generative AI methods, datasets, and evaluation metrics, providing a comprehensive overview and identifying key challenges and opportunities.

Findings

EEG-to-image models mainly use GANs, VAEs, or diffusion models.

EEG-to-text approaches increasingly utilize transformer-based language models.

EEG-to-audio methods often convert signals into mel-spectrograms for neural vocoder synthesis.

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.