发表机构
CapaCloud Corp（CapaCloud公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对梦境状态脑电图，提出PHINN-EEG拓扑时间序列框架，通过滑动窗口等提取动态贝蒂曲线，结合拓扑条件流匹配，在梦境内容分类上优于现有基准，还引入相关模型及原型，有望实现从频谱能量到相空间几何的范式转变。
AI 中文摘要
当前基于脑电图（EEG）的梦境检测依赖于功率谱密度（PSD）和统计矩特征，在DREAM数据库上实现了约0.70的接收器操作特征曲线（AUC）下的最新技术水平。我们引入了PHINN-EEG（用于EEG的持久同调启发神经网络），这是第一个用于梦境分析的拓扑时间序列框架。通过对多通道觉醒前EEG片段使用滑动窗口Takens延迟嵌入和Vietoris-Rips过滤，我们提取了表征神经活动几何结构而非仅仅其能量的动态贝蒂曲线。这些拓扑不变量与拓扑条件流匹配相结合，在DREAM数据库的1462次觉醒开放访问子集中，目标AUC为0.82 - 0.90，优于现有的PSD和catch22基准。我们还引入了用于梦境状态EEG合成的拓扑条件整流流模型，并提出了一组将拓扑与现象学梦境报告类别联系起来的候选贝蒂过渡原型。如果得到验证，这项工作代表了神经罕见事件检测从频谱能量到相空间几何的范式转变，对可穿戴BCI梦境监测具有潜在的未来意义。
英文摘要
Current electroencephalography (EEG)-based dream detection relies on power spectral density (PSD) and statistical moment features, achieving a state-of-the-art area under the receiver operating characteristic curve (AUC) of approximately 0.70 on the DREAM database (Wong et al., 2025, Nature Communications). We introduce PHINN-EEG (Persistent Homology Inspired Neural Network for EEG), the first topological time-series framework for dream mentation analysis. Using sliding-window Takens delay embeddings and Vietoris-Rips filtrations on multichannel pre-awakening EEG epochs, we extract Dynamic Betti Curves that characterize the geometric architecture of neural activity, not merely its energy. These topological invariants, combined with topology-conditioned flow matching, are analytically projected to outperform existing PSD and catch22 benchmarks, targeting AUC = 0.82-0.90 on the 1,462-awakening open-access subset of the DREAM database (drawn from a full registry of 3,191 total awakenings from 263 participants across 20 independent laboratories). We further introduce a topology-conditioned rectified flow model for dream-state EEG synthesis-with a spectral-conditioned flow model of comparable feature dimensionality as an additional ablation baseline to isolate the value of topological conditioning specifically-and propose a set of candidate Betti transition archetypes linking topology to phenomenological dream report categories, presented as an exploratory hypothesis space pending empirical validation. If validated, this work represents a paradigm shift from spectral energy to phase-space geometry in neural rare-event detection, with potential future implications for wearable BCI dream monitoring.
