用脑电图解码图像视频音频，机器学习带来新突破
Comprehensive Review of EEG-to-Output Research: Decoding Neural Signals into Images, Videos, and Audio
- 系统梳理1800篇论文，聚焦生成模型与评估方法
- 发现生成对抗网络等模型在解码中表现突出
- 适合脑机接口、神经科学领域研究者参考
脑电图（EEG）是神经科学中高时间分辨率的重要工具。近年来，机器学习与生成建模的进步推动了利用EEG重构感知体验的应用，包括图像、视频和音频。本文系统综述了EEG-to-output研究，重点分析前沿生成方法、评估指标与数据挑战。基于PRISMA指南，我们分析了1800项研究，识别出该领域的关键趋势、挑战与机遇。结果强调生成对抗网络（GANs）、变分自编码器（VAEs）和Transformer等先进模型的潜力，同时指出标准化数据集与跨被试泛化能力的迫切需求。本文提出未来研究路线图，旨在提升解码精度并拓展真实应用场景。
原文摘要 · Abstract (English)
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.
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