EEG Signal Classification with Deep Neural Networks using Visibility Graphs

Abstract

EEG signals are data presented by collecting electrical activities in the brain at a certain frequency. Today, applications using the EEG signal are implemented in many fields such as medicine, computer science, robotic. Visibility Graphs, on the other hand, are graphs where certain points are associated according to their visibility features in order to perform mapping and operations in areas such as robotics. Visibility Graphs are also used today to express signals. In this study, the EEG signals are expressed with visibility graphs after certain pre-processing. Then, the classification of the obtained graph depending on the clique and degree features was carried out by using deep artificial neural networks. EEG signals have a very noisy nature, and complex pre-processing and feature extractions are used in applications using EEG signals. In the proposed method, EEG signals are subjected to very simple pre-processing and classified with a 95% success rate.