Alzheimer's disease (AD) alters brain electrophysiology and disrupts multichannel EEG dynamics, making accurate and clinically useful EEG-based diagnosis increasingly important for screening and disease monitoring. However, many existing approaches rely on black-box classifiers and do not explicitly model the underlying dynamics that generate observed signals. To address these limitations, we propose LERD, an end-to-end Bayesian electrophysiological neural dynamical system that infers latent neural events and their relational structure directly from multichannel EEG without event or interaction annotations. LERD combines a continuous-time event inference module with a stochastic event-generation process to capture flexible temporal patterns, while incorporating an electrophysiology-inspired dynamical prior to guide learning in a principled way. We further provide theoretical analysis that yields a tractable bound for training and stability guarantees for the inferred relational dynamics. Extensive experiments on synthetic benchmarks and two real-world AD EEG cohorts demonstrate that LERD consistently outperforms strong baselines and yields physiology-aligned latent summaries that help characterize group-level dynamical differences.

翻译：阿尔茨海默病（AD）会改变大脑电生理特性并破坏多通道脑电图（EEG）动力学，这使得基于EEG的准确且具有临床实用性的诊断对于疾病筛查和监测日益重要。然而，现有许多方法依赖于黑盒分类器，并未显式建模生成观测信号的基础动力学。为应对这些局限，我们提出了LERD，一种端到端的贝叶斯电生理神经动力学系统，它能够直接从多通道EEG中推断潜在的神经事件及其关系结构，而无需事件或交互标注。LERD将连续时间事件推断模块与随机事件生成过程相结合，以捕捉灵活的时间模式，同时融入一个受电生理学启发的动力学先验，以原则性的方式指导学习。我们进一步提供了理论分析，为训练推导出一个易于处理的边界，并为推断出的关系动力学提供了稳定性保证。在合成基准测试和两个真实世界AD EEG队列上的大量实验表明，LERD始终优于强基线方法，并能产生与生理学对齐的潜在摘要，有助于表征群体水平的动力学差异。