The accurate characterization of how different brain structures interact in terms of both structural and functional networks is an area of active research in neuroscience. A better understanding of these interactions can potentially lead to targeted treatments and improved therapies for many neurological disorders, such as epilepsy, which alone affects over 65 million people worldwide. The study of functional connectivity networks in epilepsy, which is characterized by abnormalities in brain electrical activity, will help to provide new insights into the onset and progression of this complex neurological disorder. In this chapter, we discuss statistical signal processing techniques and their use in determining functional connectivity among brain regions exhibiting epileptic activity. We also discuss computational challenges associated with deriving functional connectivity measures from neurological Big Data, and we introduce our highly scalable signal processing pipeline for quantifying functional connectivity with the goal of addressing these challenges and potentially advancing understanding of the underlying mechanisms of epilepsy. This pipeline makes use of a novel signal data format that facilitates storing and retrieving data in a distributed computing environment. We conclude the chapter by describing our current activities and proposed plans for improving our computational pipeline, such as the inclusion of biomedical ontologies for semantic annotation in order to facilitate the integration and retrieval of signal data.