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EEG sensors are used to capture electric waves from the human brain. One of its main purposes is to sample signal from Epilepsy patients, to sample their seizures. Nowadays, seizures detection done manually by professional physicians, which examine EEG signals and recognize when the seizures accrued. There is a need to build automated algorithms to identify seizures. Accurate evaluation, pre-surgery assessments, and emergency alerts for medical aid all depend on the detection of the onset of seizures.

The project's main goal is to classify time segments, by processing EEG signals, into two groups: Epileptic seizures and Non-epileptic. We are using kernel based geometric methods, based on Diffusion Maps and Alternating Diffusion Maps and their improvements. This document describes all the methods we used. The Improved Alternating Diffusion method yielded the best results.