Comprehensive Review of EEG-to-Output Research: Decoding Neural Signals into Images, Videos, and Audio

TL;DR

This systematic review surveys the EEG-to-output decoding landscape, focusing on generative approaches that translate neural signals into images, audio, and video. Using PRISMA, it analyzes 1800 studies from 2015–2024 and refines them to a high-quality subset across modalities, highlighting GAN-, VAE-, and transformer-based methods. Key findings identify strong image and video reconstruction capabilities alongside challenges in cross-subject generalization, dataset standardization, and ethical considerations, for which a forward-looking roadmap is proposed. The work emphasizes the need for standardized benchmarks, multimodal integration, and interpretable models to translate EEG-to-output systems toward real-world, responsible applications.

Abstract

Electroencephalography (EEG) is an invaluable tool in neuroscience, offering insights into brain activity with high temporal resolution. Recent advancements in machine learning and generative modeling have catalyzed the application of EEG in reconstructing perceptual experiences, including images, videos, and audio. This paper systematically reviews EEG-to-output research, focusing on state-of-the-art generative methods, evaluation metrics, and data challenges. Using PRISMA guidelines, we analyze 1800 studies and identify key trends, challenges, and opportunities in the field. The findings emphasize the potential of advanced models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers, while highlighting the pressing need for standardized datasets and cross-subject generalization. A roadmap for future research is proposed that aims to improve decoding accuracy and broadening real-world applications.