Abstract
Electrooculography (EOG) is considered as the most stable physiological signal in the development of human–computer interface (HCI) for detecting eye-movement variations. EOG signal classification has gained more traction in recent years to overcome physical inconvenience in paralyzed patients. In this paper, a robust classification technique, such as eight directional movements is investigated by introducing a concept of buffer along with a variation of the slope to avoid misclassification effects in EOG signals. Blinking detection becomes complicated when the magnitude of the signals are considered. Hence, a correction technique is introduced to avoid misclassification for oblique eye movements. Meanwhile, a case study has been considered to apply these correction techniques to HCI baseball game to learn eye-movements.
Highlights
The importance of eye movement tracking along with humancomputer interaction (HCI) has been investigated in this paper
EOG signal is considered in this study to differentiate various eye-movements of the subjects
Overall computational time has been reduced by eliminating down sampling of the EOG signals
Summary
The importance of eye movement tracking along with humancomputer interaction (HCI) has been investigated in this paper. This approach has remained a promising method which is used in recent years to detect and analyze eye movements. Electrooculography (EOG) is an inexpensive technique used in recent years to record eye movements [1]. EOG signal classification is considered as the most useful control signals for human-computer interface [2]. Eight directional eye movement classification algorithm is an effective way to analyze the aftermath effect of noise in EOG signals. A thorough understanding of various characteristics of eye movements leads to a better understanding of eye-movement detection algorithm
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