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arXiv · 2609.36773

NeuroDyn-EEG: An Interpretable Pre-trained Model for EEG Based on Neural Dynamics

Yi Cui·Tong Zhao·Jiaxin Lei·Chuyi Yang·Yifan Cui·Ling Zhang·Yuxiang Yan·Bo Hong
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Abstract

Clinical scalp electroencephalography (EEG) offers a noninvasive window into neural dynamics of neuropsychiatric disorders. However, discriminative deep models often lack anatomically indexed physiological interpretability. We propose NeuroDyn-EEG, a pretraining framework integrating generative priors from neural dynamics. It couples an extended Jansen-Rit neural mass model, leadfield-based source projection, and simulation-based parameter inversion. Trained on synthetic parameter-EEG pairs within physiological ranges, NeuroDyn-EEG estimates 11 regional parameter families across 90 AAL regions plus one global parameter from standard 19-channel EEG, using only ~2.43M trainable parameters. We evaluate the framework across three levels. First, controlled simulations demonstrate robust parameter recovery under diverse noise conditions, while real resting-state EEG evaluations confirm spectral and phase consistency in an inverse-forward closed loop. Second, on four clinical benchmarks (AD65, PD31, Figshare MDD, and TUAB), NeuroDyn-EEG achieves competitive classification performance, securing the highest BACC, AUROC, and AUCPR on PD31 and MDD, and highest BACC on AD65. Third, post hoc regional analyses reveal disease-specific alterations: local synaptic connectivity C_1 involves the most altered regions in AD65, whereas the firing threshold theta ranks first in MDD, offering testable mechanistic hypotheses. Overall, NeuroDyn-EEG maps scalp EEG to anatomically indexed dynamical parameters, bridging representation learning and mechanistic neurophysiology. Code: https://github.com/Gnosis-Neurodynamics/NeuroDyn-EEG.

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Yi Cui, Tong Zhao, Jiaxin Lei, Chuyi Yang, Yifan Cui, Ling Zhang, Yuxiang Yan, Bo Hong. 2026-09-29. NeuroDyn-EEG: An Interpretable Pre-trained Model for EEG Based on Neural Dynamics. https://arxiv.org/abs/2609.36773

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