2025/02/26 by Panchagnula, Tapasvi
#68T07 #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #I.2.6 #I.2.7 #J.3 #Machine Learning (cs.LG)
paper · doi:10.48550/arxiv.2503.05778
Dream narratives provide a unique window into human cognition and emotion, yet their systematic analysis using artificial intelligence has been underexplored. We introduce DreamNet, a novel deep learning framework that decodes semantic themes and emotional states from textual dream reports, optionally enhanced with REM-stage EEG data. Leveraging a transformer-based architecture with multimodal attention, DreamNet achieves 92.1% accuracy and 88.4% F1-score in text-only mode (DNet-T) on a curated dataset of 1,500 anonymized dream narratives, improving to 99.0% accuracy and 95.2% F1-score with EEG integration (DNet-M). Strong dream-emotion correlations (e.g., falling-anxiety, r = 0.91, p < 0.01) highlight its potential for mental health diagnostics, cognitive science, and personalized therapy. This work provides a scalable tool, a publicly available enriched dataset, and a rigorous methodology, bridging AI and psychological research.