Analysis of Electrooculogram using SVM Classifier

Abstract

In recent years, Electrooculography (EOG) is one of the technique to measure biomedical signal for analyzing the eye movement patterns in corneo-retinal which located between the front and back of the human eye. This electrographic signal measure the eye movements from left to right or top to bottom which create the electrical deflections. In this paper, the EOG data was obtained from five participants and the respective data was preprocessed before feature extractions that help to reduce the data dimensions. These features are extracted by Support Vector Machine (SVM) and the performance evaluation was analyzed with classification accuracy of 70.50%.