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

TL;DR

This review paper analyzes recent advancements in decoding EEG signals into images, videos, and audio using generative models, highlighting key trends, challenges, and future directions in the field.

Contribution

It systematically reviews over 1800 studies, identifying state-of-the-art generative methods, evaluation metrics, and data challenges in EEG-to-output research.

Findings

Generative models like GANs, VAEs, and Transformers are promising.

Standardized datasets and cross-subject generalization are critical challenges.

Future research should focus on improving decoding accuracy and real-world 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.