Abstract
This study presents DreamDiffusion, an innovative approach to produce high-quality images straight from electroencephalogram (EEG) brain signals, eliminating the need for thought-to-text translation. By harnessing pre-trained text-to-image models, DreamDiffusion integrates temporal masked signal modeling to adeptly pre-train the EEG encoder, ensuring accurate and dependable EEG data representation. Moreover, by integrating the CLIP image encoder, this method fine-tunes the alignment of EEG, text, and image embeddings, even with a scant amount of EEG-image pairs. Effectively navigating the complexities inherent in EEG-based image creation, such as data noise, limited content, and personal variances, DreamDiffusion showcases promising outcomes. Both quantitative and qualitative assessments validate its efficacy, marking a considerable advancement in the realm of efficient, affordable "thought-to-image" conversions, with promising implications in both neuroscience and computer vision.
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