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.

翻译：本文介绍了DreamDiffusion，一种直接从脑电图（EEG）信号生成高质量图像的新方法，无需将思想转化为文本。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，以获得有效且鲁棒的EEG表征。此外，该方法进一步借助CLIP图像编码器提供额外监督，以在有限的EEG-图像对条件下更好地对齐EEG、文本和图像嵌入。总体而言，所提出的方法克服了利用EEG信号进行图像生成所面临的挑战，如噪声、信息有限和个体差异，并取得了令人满意的结果。定量和定性结果表明，该方法有效，是迈向便携且低成本的“思想到图像”的重要一步，在神经科学和计算机视觉领域具有潜在应用价值。