Simple and Robust Eye Movements Detection Method

Abstract

The aim of this research is to develop a signal processing method to detect four eye movements, such as looking up, down, left, right and blinking. This new method has a couple of features in comparison with the recent eye movement detection algorithms. Most of the recent algorithms require a De-noising stage, which is not required in this work. In addition, the suggested algorithm can be considered simple and robust in noisy environment in contrast with other algorithms. In this paper, short-time averaging method is proposed to process and to extract parameters from EOG signals. Moreover, adaptive threshold is applied to classify EOG pulses. The purpose of the adaptive threshold is to enhance the performance of the algorithm in a noisy background. Simulation results are based on real-life EOG signals, where these signals were recorded using an Electrooculography system. The results show that the proposed algorithm has a stable performance HR=100 % and FR=0% with SNR greater than 2dB. The average performance with SNR=0.5 dB is about HR= 90.21% and FR= 4.88%.