ODEBrain: Continuous-Time EEG Graph for Modeling Dynamic Brain Networks

2026-02-26

AI Summary

This work addresses the limitations of conventional discrete-time approaches in accurately capturing the instantaneous nonlinear dynamics of electroencephalography (EEG) signals and their tendency to accumulate prediction errors. To overcome these challenges, we propose the first continuous-time modeling framework that integrates Neural Ordinary Differential Equations (Neural ODEs) with the graph structure of EEG data. By embedding spatiotemporal-frequency features into spectral graph nodes, our method enables high-fidelity modeling of the brain’s continuous latent dynamics. The framework supports inference of brain states at arbitrary time points and demonstrates superior performance over existing methods in EEG dynamic prediction tasks, exhibiting enhanced robustness and generalization capability.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Modeling neural population dynamics is crucial for foundational neuroscientific research and various clinical applications. Conventional latent variable methods typically model continuous brain dynamics through discretizing time with recurrent architecture, which necessarily results in compounded cumulative prediction errors and failure of capturing instantaneous, nonlinear characteristics of EEGs. We propose ODEBRAIN, a Neural ODE latent dynamic forecasting framework to overcome these challenges by integrating spatio-temporal-frequency features into spectral graph nodes, followed by a Neural ODE modeling the continuous latent dynamics. Our design ensures that latent representations can capture stochastic variations of complex brain states at any given time point. Extensive experiments verify that ODEBRAIN can improve significantly over existing methods in forecasting EEG dynamics with enhanced robustness and generalization capabilities.

Problem

• EEG dynamics
• continuous-time modeling
• neural population dynamics
• nonlinear characteristics
• cumulative prediction errors

Innovation

• Neural ODE
• EEG dynamics
• continuous-time modeling
• graph neural networks
• brain network