Combined EEG/MEG Source Reconstruction of Epileptic Activity using a Two-Phase Spike Clustering Approach

In recent years, several approaches have been introduced for estimating the spike onset zone within the irritative zone in epilepsy diagnosis for presurgical planning. One important direction utilizes source analysis from combined electroencephalography (EEG) and magnetoencephalography (MEG), EMEG, leveraging the benefits from the complementary properties of the two modalities. For EMEG source reconstruction, an average across the annotated epileptic spikes is often used to improve the signal-to-noise-ratio (SNR). In this contribution, we propose a two-phase clustering of interictal spikes with unsupervised learning methods, namely Self Organizing Maps (SOM) and K-means. In addition, we investigate the accuracy of combined EMEG source analysis on the sorted activity, using an individualized (with regard to both geometry and conductivity) six-compartment finite element head model with calibrated skull conductivity and white matter conductivity anisotropy. The results indicate that SOM eliminates the random variations of K-means and stabilizes the clustering efficiency. In terms of source reconstruction accuracy, this study demonstrates that the combined use of modalities reveals activity around two focal cortical dysplasias (FCDs), of one epilepsy patient, one in the right frontal area and one smaller in the left premotor cortex. It is worth mentioning that only EMEG could localize the left premotor FCD, which was then also found in surgery to be the responsible for triggering the epilepsy.