LERD: Latent Event-Relational Dynamics for Neurodegenerative Classification

• URL: http://arxiv.org/abs/2602.18195v1
• Date: Fri, 20 Feb 2026 13:03:40 GMT
• Title: LERD: Latent Event-Relational Dynamics for Neurodegenerative Classification
• Authors: Hairong Chen, Yicheng Feng, Ziyu Jia, Samir Bhatt, Hengguan Huang,
• Abstract summary: Alzheimer's disease (AD) alters brain electrophysiology and disrupts multichannel EEG dynamics.<n>We propose LERD, an end-to-end electrophysiological neural dynamical system that infers latent neural events and their relational structure directly from multichannel EEG.
• Score: 19.992574981355247
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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