Developing a Method for Classifying Electro-Oculography (EOG) Signals Using Deep Learning

Recently, a significant increase appears in the number of patients with severe motor disabilities even though the cognitive parts of their brains are intact. These disabilities prevent them from being able to move all their limbs except for the movement of their eyes. This creates great difficulty in carrying out the simplest daily activities, as well as difficulty in communicating with their surrounding environment. With the advent of Human Computer Interfaces (HCI), a new method of communication has been found based on determining the direction of eye movement. The eye movement is recorded by Electro-oculogram (EOG) using a set of electrodes placed around the eye horizontally and vertically. In this work, The horizontal and vertical EOG signals are filtered and analyzed to determine six eye movement directions (Right, left, up, down, center, and double blinking). The deep learning models namely Residual network and ResNet-50 network have been examined. The experimental results show that the ResNet-50 network gives the best average accuracy 95.8%.