用AI分析梦话里的情绪和主题，还能结合脑电数据提升准确率
DreamNet: A Multimodal Framework for Semantic and Emotional Analysis of Sleep Narratives
- 基于多模态注意力机制的Transformer框架，融合文本与脑电数据
- 纯文本模式下准确率达92.1%，加入脑电后提升至99.0%
- 适用于心理健康评估、认知研究及个性化心理干预
梦境叙事为理解人类认知与情绪提供了独特视角，但其在人工智能系统中的系统性分析仍不充分。本文提出DreamNet，一种新型深度学习框架，可从文本梦境报告中解码语义主题与情绪状态，并可选地结合REM期脑电图（EEG）数据进行增强。该框架采用基于Transformer的多模态注意力结构，在包含1,500份匿名梦境叙事的定制数据集上，纯文本模式（DNet-T）实现92.1%准确率与88.4% F1分数；融合脑电数据后（DNet-M）进一步提升至99.0%准确率与95.2% F1分数。强梦境-情绪相关性（如坠落-焦虑，r = 0.91, p < 0.01）凸显其在心理健康诊断、认知科学及个性化治疗中的潜力。本研究提供可扩展工具、公开可用的增强数据集与严谨方法论，推动人工智能与心理学研究的融合。
原文摘要 · Abstract (English)
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.
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