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

• URL: http://arxiv.org/abs/2602.23285v2
• Date: Fri, 27 Feb 2026 02:54:11 GMT
• Title: ODEBrain: Continuous-Time EEG Graph for Modeling Dynamic Brain Networks
• Authors: Haohui Jia, Zheng Chen, Lingwei Zhu, Rikuto Kotoge, Jathurshan Pradeepkumar, Yasuko Matsubara, Jimeng Sun, Yasushi Sakurai, Takashi Matsubara,
• Abstract summary: ODEBRAIN can improve significantly over existing methods in forecasting EEG dynamics with enhanced generalization and capabilities.<n>Our design ensures that latent representations can capture variations of complex brain states at any given time point.
• Score: 33.66198565629555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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