Classification of Electroencephalogram signals using Artificial Neural Networks

The study of Artificial Neural Networks (ANN) has been fascinating over the years and its development has strongly grown in recent years. The neural networks methods have become to be increasingly convincing for solving complex problems, through artificial intelligence. In particular, this work, focused on the development of an artificial neural network for identifying diseases: Parkinson´s, Huntington´s and Amyotrophic Lateral Sclerosis, based on signals from the Electroencephalogram (EEG). The project was developed through a number of operations implemented in Matlab. The Fourier transform was seen as the main technique of signal processing, in order to analyze and diagnose diseases in the study. The work consisted first in the EEG signals to serve as an entry into the ANN in order to reveal a distinctive feature in the different diseases, and then, create an ANN architecture capable to distinguish the diseases. For this purpose 4 methodologies were used with different processing of the EEG signal. The 4 methodologies are compared in this paper.