Robust Eye Movement Recognition Using EOG Signal for Human-Computer Interface

Electrooculography (EOG) signal is one of the useful biomedical signals. Development of EOG signal as a control signal has been paid more increasing interest in the last decade. In this study, we are proposing a robust classification algorithm of eight useful directional movements that it can avoid effect of noises, particularly eye-blink artifact. Threshold analysis is used to detect onset of the eye movements. Afterward, four beneficial time features are proposed that are peak and valley amplitude positions, and upper and lower lengths of two EOG channels. Suitable threshold conditions were defined and evaluated. From experimental results, optimal threshold values were selected for each parameters and classification accuracies approach to 100% for three subjects testing. To avoid the eye-blink artifact, the first derivative was additionally implemented.

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