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[2602.18195] LERD: Latent Event-Relational Dynamics for Neurodegenerative Classification
Summary
The paper presents LERD, a Bayesian neural system designed to enhance EEG-based diagnosis of Alzheimer's disease by modeling latent neural events and their dynamics directly from EEG data.
Why It Matters
Accurate EEG-based diagnosis is crucial for Alzheimer's disease monitoring. LERD addresses limitations of existing methods by providing a more interpretable model that captures the underlying dynamics of brain activity, potentially leading to better clinical outcomes.
Key Takeaways
- LERD models latent neural events from multichannel EEG data without requiring annotations.
- The system incorporates a continuous-time event inference module for flexible temporal pattern capture.
- Extensive experiments show LERD outperforms existing methods in identifying group-level dynamical differences in EEG data.
Computer Science > Machine Learning arXiv:2602.18195 (cs) [Submitted on 20 Feb 2026] Title:LERD: Latent Event-Relational Dynamics for Neurodegenerative Classification Authors:Hairong Chen, Yicheng Feng, Ziyu Jia, Samir Bhatt, Hengguan Huang View a PDF of the paper titled LERD: Latent Event-Relational Dynamics for Neurodegenerative Classification, by Hairong Chen and 4 other authors View PDF HTML (experimental) 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...
