**1. Introduction**

Epileptic seizure detection deals with the process of detecting a seizure when it occurs. The need of the day is to take forward this work to eventually predict a seizure much before it is detected as it the very nature of the seizure that it is random. This chapter discusses various methods to do the same.

The cause of disorder will remain unexplained unless a complete cure is possible and available. Two practical engineering approaches are used to research in epilepsy. The first approach involves monitoring the brain activity on multiple scales which gives us a base to understand the generation of seizures. The second approach is to model the natural properties of the brain network and manipulate these for the modulation of seizure generation.

This work mainly concentrates on amalgamation of the above approaches towards developing a closed loop device which has a feedback of brain signals to the device so that it can control interventions that stop seizures.

The main objective in this chapter is a search for a precursor for seizure prediction mainly in the preictal phase as shown in the **Figure 1**. This may have form of an identifiable, significant pattern, feature or a pattern to extract the feature.

Five techniques are used to achieve this objective. They are:

Using Lyapunov exponents. Using Cross wavelets [1]. Fourier Bessel function [2]. Wavelets [3]. EMD [4].

**Figure 1.** *Seizure prediction methodology.*
