Time Series and Interactions: Data Processing in Epilepsy Research

Computational methods can have significant contribution to epilepsy research not only through modeling, but through data analysis as well. Vast amount of neural data has opened a new era of brain research, where new data analysis methods are needed to take full advantage of the available data. Seizure zones, for example, were traditionally localized manually, using the extremely good pattern matching or mismatch recognition skills of the human brain to identify the first pathological patterns at the initiation of the epileptic seizures. Today, mathematical methods can help automate this detection and examine possible markers of the epileptic tissue, either ictal and interictal. In this chapter, we start by discussing these detection methods, as well as open questions in the area of detection algorithms for interictal spikes and high frequency oscillations. We then continue by discussing methods for analyzing continuous signals, methods that include time-frequency analysis and entropy calculations. We finish this chapter with methods for determining causal interactions among signals and how these latter methods can be used to locate the epileptic foci.