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[2602.23285] ODEBrain: Continuous-Time EEG Graph for Modeling Dynamic Brain Networks
Summary
The paper presents ODEBrain, a Neural ODE framework designed to model dynamic brain networks using continuous-time EEG data, improving forecasting accuracy and robustness over traditional methods.
Why It Matters
Understanding brain dynamics is essential for neuroscience and clinical applications. ODEBrain addresses limitations of existing models by capturing instantaneous EEG characteristics, which could enhance diagnostic and therapeutic strategies in neurology.
Key Takeaways
- ODEBrain integrates spatio-temporal-frequency features into EEG modeling.
- The framework uses Neural ODEs to capture continuous latent dynamics effectively.
- Extensive experiments demonstrate significant improvements in forecasting EEG data.
- The model enhances robustness and generalization capabilities compared to existing methods.
- This approach could lead to better understanding and treatment of neurological conditions.
Computer Science > Artificial Intelligence arXiv:2602.23285 (cs) [Submitted on 26 Feb 2026] 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 View a PDF of the paper titled ODEBrain: Continuous-Time EEG Graph for Modeling Dynamic Brain Networks, by Haohui Jia and 8 other authors View PDF HTML (experimental) 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. Subjects: Artificial Intelligence (cs.AI) Cite as...
