首个融合脑电与梦境图像的开源数据集，助力AI解码梦境。
Dream2Image : An Open Multimodal EEG Dataset for Decoding and Visualizing Dreams with Artificial Intelligence
- 收集38人31小时脑电，匹配梦境描述与AI生成图像。
- 提供觉醒前15至120秒脑电片段，共129个样本。
- 适合神经科学、AI解码与梦研究者使用。
Dream2Image是全球首个结合脑电图（EEG）信号、梦境文字记录和AI生成图像的多模态数据集。基于38名参与者超过31小时的梦境脑电记录，包含129个样本，涵盖觉醒前15、30、60、120秒的脑电活动片段、原始梦境报告及梦境的近似视觉重构。该数据集为梦境研究提供了新资源，可用于探索做梦时的神经关联、开发从脑活动解码梦境的模型，并推动神经科学、心理学与人工智能交叉研究。数据在Hugging Face和GitHub上公开获取，旨在激发研究创新，拓展脑活动解码方法。局限在于样本量较小且梦境回忆存在个体差异，可能影响泛化性。
原文摘要 · Abstract (English)
Dream2Image is the world's first dataset combining EEG signals, dream transcriptions, and AI-generated images. Based on 38 participants and more than 31 hours of dream EEG recordings, it contains 129 samples offering: the final seconds of brain activity preceding awakening (T-15, T-30, T-60, T-120), raw reports of dream experiences, and an approximate visual reconstruction of the dream. This dataset provides a novel resource for dream research, a unique resource to study the neural correlates of dreaming, to develop models for decoding dreams from brain activity, and to explore new approaches in neuroscience, psychology, and artificial intelligence. Available in open access on Hugging Face and GitHub, Dream2Image provides a multimodal resource designed to support research at the interface of artificial intelligence and neuroscience. It was designed to inspire researchers and extend the current approaches to brain activity decoding. Limitations include the relatively small sample size and the variability of dream recall, which may affect generalizability.
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