A neural mass modelling framework for evaluating EEG source localisation of seizure activity.

Abstract

This study addresses the challenge of objectively evaluating electroencephalography and magnetoencephalography (EEG/MEG) source localisation algorithms in the absence of an experimentally verifiable ground truth, specifically for the characterisation of ictal dynamics. 
Approach. A simulation framework is presented for generating biologically plausible ictal dynamics and their corresponding EEG signals to enable systematic benchmarking of source imaging approaches. Cortical seizure initiation and propagation were simulated using network-coupled neural mass (Epileptor) models and combined with realistic forward models of the human head to produce macroscopic, electrophysiological data with known ground truth under varying conditions. 
Main results. Using this dataset, we evaluated the established MN-family of source localisation methods across idealised and realistic scenarios. Existing approaches achieved reasonable spatial accuracy under high-density, noise-free conditions, but performance degraded substantially with reduced sensor coverage and added noise. This degradation was driven primarily by failures to recover source polarity, even when spatial localisation remained relatively accurate. 
Significance. These results suggest that current methods may be sufficient for identifying epileptogenic regions or tracking regional recruitment but highlight polarity reconstruction as a key limitation for studies of seizure dynamics and network organisation. The proposed framework provides a reproducible and biologically grounded testbed for the development and evaluation of electrophysiological source localisation techniques.