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

2025-02-17

AI Summary

This study systematically reviews recent advances in integrating EEG signals with generative AI, focusing on the core challenge of cross-modal generation—transforming neural activity into images, text, and speech. Methodologically, it comprehensively unifies GANs, VAEs, diffusion models, and Transformer- and contrastive-learning-based frameworks, constructing a structured technical taxonomy spanning datasets, feature encoding strategies, evaluation metrics, and application scenarios. A key contribution is the formal proposal and conceptualization of EEG-to-speech generation as a novel research direction. The work identifies critical performance bottlenecks—including low signal-to-noise ratio, high inter-subject variability, and severe label scarcity—and advocates for transferable representation learning as a viable pathway forward. Collectively, these findings provide foundational methodological guidance for advancing neural decoding theory, enabling practical assistive communication technologies, and accelerating the real-world deployment of brain–computer interfaces.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Generation
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Integration of Brain-Computer Interfaces (BCIs) and Generative Artificial Intelligence (GenAI) has opened new frontiers in brain signal decoding, enabling assistive communication, neural representation learning, and multimodal integration. BCIs, particularly those leveraging Electroencephalography (EEG), provide a non-invasive means of translating neural activity into meaningful outputs. Recent advances in deep learning, including Generative Adversarial Networks (GANs) and Transformer-based Large Language Models (LLMs), have significantly improved EEG-based generation of images, text, and speech. This paper provides a literature review of the state-of-the-art in EEG-based multimodal generation, focusing on (i) EEG-to-image generation through GANs, Variational Autoencoders (VAEs), and Diffusion Models, and (ii) EEG-to-text generation leveraging Transformer based language models and contrastive learning methods. Additionally, we discuss the emerging domain of EEG-to-speech synthesis, an evolving multimodal frontier. We highlight key datasets, use cases, challenges, and EEG feature encoding methods that underpin generative approaches. By providing a structured overview of EEG-based generative AI, this survey aims to equip researchers and practitioners with insights to advance neural decoding, enhance assistive technologies, and expand the frontiers of brain-computer interaction.

Problem

• Bridging EEG signals and Generative AI
• Enhancing EEG-based multimodal generation
• Advancing neural decoding and assistive technologies

Innovation

• EEG signals integrated with Generative AI
• GANs and VAEs for EEG-to-image generation
• Transformer models for EEG-to-text synthesis