Epilepsy is a neurological disorder characterized by recurrent, unprovoked seizures that produce a distinct signature of hypersynchronous neural activity in electroencephalographic (EEG) recordings. This phenomenon is generally attributed to hyperexcitability caused by an imbalance of excitatory and inhibitory activity in the brain. This model, however, does not sufficiently explain how seizures are generated since treatments that curb excitation and increase inhibition do not adequately treat 1/3 of epilepsy patients. These patients often turn to neurosurgical options to remove the culprit brain area suspected to be the origin of seizure activity. However, 1/4 of surgical patients fail to find adequate seizure relief, bringing into question the validity of how we identify, localize, and even conceive of epileptogenicity. The search for epileptogenic tissue is further complicated by the fact that seizures exhibit significantly different characteristics depending on the scale at which they are observed. Because observations do not readily translate across scales, accurate interpretation of clinical recordings requires in-depth understanding of seizure dynamics at various scales as well as knowledge of how these dynamics translate into recorded signals. To this end, the overall goal of this thesis is to gain a multiscale understanding of epileptiform activity using a combinination of signal analysis and mathematical modeling to develop mechanistically meaningful interpretations of clinical EEGs.