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

TL;DR

ODEBRAIN, a Neural ODE latent dynamic forecasting framework to overcome challenges in forecasting EEG dynamics with enhanced robustness and generalization capabilities, is proposed.

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.

Figures (9)

• Figure 1: (Left) Continuous EEG real-time neuronal activity recordings. (Mid) Recurrent-based methods employ discrete modeling. (Right) ODE provides a continuous representation for forecasting neuronal population dynamics.
• Figure 2: Continuous neural dynamics modeling via ODEBRAIN with graph forecasting. In stage 1, multi-channel EEG signals are encoded into spectral graph snapshots and fused with raw features to construct noise-robust initial states for ODE integration, predicting future spectral graphs. In stage 2, ODEBRAIN propagates latent states through time, generating dynamic field $f$ that captures continuous trajectories. Finally, future graph node embeddings are obtained by $z_T$ and compared with the ground-truth graph nodes.
• Figure 3: The full structure of the temporal-spatial ODE solving. (a)RK-4 step numerical solver. (b) Procedure of temporal-spatial $f_{\theta}$.
• Figure 4: Visualization results between the multichannel EEG signal (upper and lower) and its latent dynamic field $f_{\theta}$ (middle) obtained by ODEBRAIN . Local minima appearing in (a) and (c) indicate rapid changes, corresponding to seizure states.
• Figure 5: Visualizing learned dynamic fields between our spatial-temporal(ST)-ODE solver and the frequency (F)-ODE solver.