Classification of EEG signals to detect epilepsy problems

Epilepsy is a condition that affects 0.6-0.8% of the world population, rendering it the most common neurological disorder after stroke. It is characterized by recurrent unprovoked seizures, due to abnormal, excessive or synchronous neuronal activity in the brain and by a vast range of causes, triggering events, symptoms and brain locations where the seizures originate.This project is about detecting epilepsy problems using the electroencephalogram (EEG) data acquisition system. In order to do that a number of parameters was extracted from EEG signals using the MATLAB software. Then these parameters were used in the classification of the signals via the Feedforward Neural Network method in order to make the right diagnostic of the problem. In addition, this project presents four tests done in order to compare the performance of several parameters and to select the most efficient one.