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Eye movement recognition by grey wolf optimization based neural network

The Human-Computer Interface (HCI) systems are recently being regulated by the Electrooculography (EOG) signals, which preserves the information related to the eye movements. Along with the diverse investigations related with this subject, this paper proposes a novel model for recognizing the eye movements from EOG signals using Grey Wolf Optimization (GWO) based Neural Network (NN). GWO here is used to reduce the error function of the classifier outcome. Further, it compares the performance of the proposed method with the conventional method, i.e. NN with traditional learning algorithm. The results reveal the superior performance of the proposed method with the error minimization analysis, gradient analysis and recognition performance analysis in terms of several performance measures.