Several studies have shown that in people with epilepsy, brain network features computed from interictal scalp EEG recordings could serve as potential biomarkers for epilepsy diagnosis. In addition, evolving brain networks derived from intracranial EEG recordings have shown topological changes prior to seizure occurrence.

In this small project we investigate the daily evolution of brain network features derived from multi-day scalp EEG and examine whether they change prior to seizure occurrence.

We observed a statistically significant difference (p-value <0.05 corrected for multiple comparisons) of the brain network metrics between the day before the seizure occurrence when compared to previous days. Brain network distributions were studied in multiple frequency bands and alpha and theta band were found to be the more informative.

Interictal scalp EEG data may be used for the characterization of topological changes that occur prior to seizures. In addition, they show promise as potential biomarkers for the estimation of seizure risk and their incorporation in the study pipelines for a long-term wearable EEG mobile monitoring for epilepsy forecasting and management.

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