Effect of Delay in EOG Signals for Eye Movement Recognition

Abstract

Electrooculogram (EOG) signals as a part of human-controlled interface (HCI) is proposed for detecting the relevant information in EOG with and without delay in movement of eyes. The performance of eye movements is studied with the accuracy in identification of information along with single and double blink. The algorithm consists of a simple first order derivative, threshold windowing technique, and pattern recognition. The EOG pattern recognition was studied with time domain features mean value (MV) and ensemble of MV and zero crossing (ZC). The highest average classification accuracy of 85% and 84.4% is obtained from continuous movement of eyes for three classes (L, R, DB and L, R, SB) with two time-domain features. Further, the accuracy of 90% and 88% from two eye movement detection is obtained.