Dream2Image : An Open Multimodal EEG Dataset for Decoding and Visualizing Dreams with Artificial Intelligence
This dataset addresses the problem of limited resources for dream decoding research in neuroscience and AI, though it is incremental as it builds on existing brain activity decoding methods.
The authors introduced Dream2Image, the first multimodal dataset combining EEG signals, dream transcriptions, and AI-generated images from 38 participants and 31 hours of recordings, providing 129 samples for decoding dreams from brain activity.
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.