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Neural population models for EEG: From Canonical models to alternative model structures
Nina Omejc1,2, Sabin Roman1, Ljupčo Todorovski3
1Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia.
Plos Computational Biology
|August 6, 2026
Summary
This study explores neural population models for electroencephalography (EEG) data. Findings suggest EEG data constrains plausible brain activity models but doesn't uniquely identify one, highlighting the power of grammar-based model discovery.
Area of Science:
- Computational Neuroscience
- Systems Neuroscience
- Biophysics
Background:
- Neural population models are crucial for interpreting electroencephalography (EEG) data.
- The relationship between EEG signals and population-level neural mechanisms is not well understood.
- It is unclear if EEG data uniquely determines population-level models or if multiple models can fit equally well.
Purpose of the Study:
- To investigate whether EEG data can support a uniquely plausible population-level mechanism.
- To explore the space of neural population models for EEG data using comparative analysis and grammar-based generation.
- To identify the best-performing model architectures for explaining EEG spectra.
Main Methods:
- Assembled 17 canonical neural mass and phenomenological models into a shared structural space.
- Developed ENEEGMA (Exploring Neural EEG Model Architectures), a Julia-based framework for grammar-based model generation, simulation, and parameter optimization.
- Fit canonical and generated models to EEG independent-component spectra from resting state and steady-state visual evoked potentials (SSVEP) datasets.
Main Results:
- Canonical models formed six structural clusters, with compact, low-dimensional polynomial oscillators performing best overall.
- Generalized Montbrió-Pazó-Roxin, FitzHugh-Nagumo, and Stuart-Landau models showed the best balance of fit quality, stability, and simplicity.
- Grammar-based exploration generated compact models competitive with canonical ones, with a generated cluster showing the strongest Bayesian expected rank for SSVEP fits.
Conclusions:
- EEG spectra constrain classes of plausible population-level dynamical architectures without uniquely determining them.
- Grammar-based model exploration offers a principled, data-driven framework for discovering EEG-constrained models.
- The study expands the understanding of viable EEG node models beyond traditional formulations.
