EOG Region Detection using Fuzzy k-NN for Virtual Reality Applications

Virtual Reality (VR) systems have become widespread for a decade with the mass production of VR headsets. Advancement in the VR industry benefits both biomedical and computer gaming fields to create better Human-Computer Interface (HCI) applications. In this study, Electrooculogram (EOG) signals are studied on a calibrated A4 paper to simulate reading and tracking eye movement in different regions for VR user interface applications. For that reason, eye activity features from EOG are used to identify relative 2D spatial coordinates and classified with the fuzzy k-Nearest Neighbor (fuzzy k-NN) method. Within the experimental setup, different behaviors such as blinking and depth focus change signals are recorded with constant depth regional borders are analyzed on an A4 paper with reading eye movement recordings. In experimental results, fuzzy k-NN classification results are obtained from observed regional eye movement. The study shows that the fuzzy k-NN method to detect regions at a reading distance is feasible for user interface applications in VR. So, by setting rendering focus at the detected regional area, eye strain can be reduced during prolonged VR sessions especially when reading and/or on user interfaces.