夢境擴散：從腦 EEG 信號生成高質量圖像

摘要

本文介紹了DreamDiffusion，一種從腦電圖（EEG）信號直接生成高質量圖像的新方法，無需將思維轉換為文本。DreamDiffusion利用預先訓練的文本到圖像模型，並採用時間遮罩信號建模來預先訓練EEG編碼器以獲得有效且穩健的EEG表示。此外，該方法進一步利用CLIP圖像編碼器提供額外監督，以更好地對齊EEG、文本和圖像嵌入，並在有限的EEG-圖像對中取得更好的結果。總的來說，所提出的方法克服了使用EEG信號進行圖像生成時的挑戰，如噪音、信息有限和個體差異，並取得了令人期待的結果。定量和定性結果顯示所提出的方法作為實現可攜式和低成本“思維到圖像”的重要一步，具有潛在的應用於神經科學和計算機視覺。

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.

夢境擴散：從腦 EEG 信號生成高質量圖像

DreamDiffusion: Generating High-Quality Images from Brain EEG Signals

摘要

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