Since the early 2000s, the growing field of computational neuroscience has shown remarkable applicability in the study of epilepsy. A number of different and complementary approaches have been applied to brain signals obtained with scalp and invasive electroencephalography (EEG) to address a variety of fundamental and clinical problems. Historically, researchers have focused on overt changes in brain electrical signals, which can be detected using signal processing techniques. More recent advances have also shown that connectivity and network-level effects can provide critical information that complements the classical brain regional perspective. Thus, the modern toolkit for epilepsy electrophysiology now includes complex systems approaches such as network science (e.g., graph theory), nonlinear signal processing, information theory, and machine learning techniques. Complex systems approaches have made their contribution to our understanding of epilepsy and to the development of new tools that might improve its diagnosis and treatment.