NeuroDyn EEG estimates brain parameters from scalp recordings
NeuroDyn-EEG: An Interpretable Pre-trained Model for EEG Based on Neural Dynamics
Neural and Evolutionary Computing
Summary
Decoding brain activity from EEG signals is hard because brain processes are complex and hard to interpret. The authors created NeuroDyn-EEG, a model that uses simulated brain dynamics to learn how brain signals relate to underlying brain properties. This model connects EEG data to specific brain regions and their neural functions, making results easier to understand. It performs well in distinguishing neurological conditions and suggests possible brain changes linked to diseases.
What this means in practice
- •For clinical neurophysiology teams: Estimate region-specific neural parameters from standard EEG to assist in diagnosing neurological disorders.
- •For brain-computer interface developers: Incorporate interpretable neural parameters from EEG into adaptive neurotechnology for improved user feedback.
Authors
Yi Cui, Tong Zhao, Jiaxin Lei, Chuyi Yang, Yifan Cui, Ling Zhang, Yuxiang Yan, Bo Hong
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.