Low computational complexity classifier based on the maximum relative global peak for the classification of EOG signals
Akram A. Marseet, Shitij Kumar, Ferat Sahin · 2017
In this paper, a new low computational complexity machine learning classifier is developed to detect eye movements from an Electrooculography (EOG) signal. The proposed classier is based on finding the maximum relative global peak of a set of signals that are decomposed from the original EOG signals. Unlike many other classifiers, the proposed classifier does not depend on threshold signal levels or spectrum analysis of the EOG signals which require nonlinear and complex operations. The proposed classifier uses four predefined functions which are divided into two groups to distinguish between the horizontal and vertical eye movements. The new proposed classifier can be used for on low powerful boards because it does not require high speed processors where it needs to calculate only one optimum parameter that maximizes the global accuracy. The performance of the proposed classifier is better and faster by 40% than the SVM classifier. In addition, there is a misclassification between two classes only rather than three classes.