梦境扩散：从脑电图信号生成高质量图像

摘要

本文介绍了DreamDiffusion，一种新颖的方法，可以直接从大脑脑电图（EEG）信号生成高质量图像，无需将思维转化为文本。DreamDiffusion利用预训练的文本到图像模型，并采用时间掩码信号建模来预训练EEG编码器，以实现有效和稳健的EEG表示。此外，该方法进一步利用CLIP图像编码器提供额外监督，以更好地对齐具有限制的EEG-图像对的EEG、文本和图像嵌入。总体而言，所提出的方法克服了使用EEG信号进行图像生成时的挑战，如噪声、信息有限和个体差异，并取得了令人满意的结果。定量和定性结果展示了所提出方法的有效性，是朝着便携和低成本的“思维到图像”方法迈出的重要一步，具有潜在的在神经科学和计算机视觉领域的应用。

English

This paper introduces DreamDiffusion, a novel method for generating high-quality images directly from brain electroencephalogram (EEG) signals, without the need to translate thoughts into text. DreamDiffusion leverages pre-trained text-to-image models and employs temporal masked signal modeling to pre-train the EEG encoder for effective and robust EEG representations. Additionally, the method further leverages the CLIP image encoder to provide extra supervision to better align EEG, text, and image embeddings with limited EEG-image pairs. Overall, the proposed method overcomes the challenges of using EEG signals for image generation, such as noise, limited information, and individual differences, and achieves promising results. Quantitative and qualitative results demonstrate the effectiveness of the proposed method as a significant step towards portable and low-cost ``thoughts-to-image'', with potential applications in neuroscience and computer vision.

梦境扩散：从脑电图信号生成高质量图像

DreamDiffusion: Generating High-Quality Images from Brain EEG Signals

摘要

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