This chapter offers an overview of core topics in epilepsy research from a complex systems perspective, starting from single-node studies up to network theory analysis. In its first part, the chapter reviews the advances in EEG biomarkers for epileptic network mapping in both invasive and scalp EEG, including univariate epileptogenic indexes linear connectivity measures within the multivariate autoregressive model, non-linear correlation measures and graph-theoretical properties. In the second part, ...
This chapter offers an overview of core topics in epilepsy research from a complex systems perspective, starting from single-node studies up to network theory analysis. In its first part, the chapter reviews the advances in EEG biomarkers for epileptic network mapping in both invasive and scalp EEG, including univariate epileptogenic indexes linear connectivity measures within the multivariate autoregressive model, non-linear correlation measures and graph-theoretical properties. In the second part, the chapter covers some of the initial contributions to characterize network dynamics in epilepsy. Particular attention is paid at the seizure prediction problem, using classic dynamical systems approaches as well as the most recent machine-learning prediction algorithms and network-science studies unraveling signatures of the transition from interictal to ictal activity.
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