ODEBrain: Continuous-Time EEG Graph for Modeling Dynamic Brain Networks
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866908855166828544 |
|---|---|
| author | Jia, Haohui Chen, Zheng Zhu, Lingwei Kotoge, Rikuto Pradeepkumar, Jathurshan Matsubara, Yasuko Sun, Jimeng Sakurai, Yasushi Matsubara, Takashi |
| author_facet | Jia, Haohui Chen, Zheng Zhu, Lingwei Kotoge, Rikuto Pradeepkumar, Jathurshan Matsubara, Yasuko Sun, Jimeng Sakurai, Yasushi Matsubara, Takashi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_23285 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ODEBrain: Continuous-Time EEG Graph for Modeling Dynamic Brain Networks Jia, Haohui Chen, Zheng Zhu, Lingwei Kotoge, Rikuto Pradeepkumar, Jathurshan Matsubara, Yasuko Sun, Jimeng Sakurai, Yasushi Matsubara, Takashi Artificial Intelligence 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. |
| title | ODEBrain: Continuous-Time EEG Graph for Modeling Dynamic Brain Networks |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2602.23285 |