Classification Algorithm for Saccadic Oculomotor Behavior
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This paper presents a detection algorithm that allows automatic classification of hypermetric and hypometric oculomotor plant behavior in cases when saccadic behavior of the oculomotor plant is assessed during the course of the step stimulus. Such behavior can be classified with a number of oculomotor plant metrics represented by the number of overshoots, undershoots, corrected undershoots/overshoots, multi-corrected overshoots/undershoots. The algorithm presented in this paper allows for the automated classification of nine oculomotor plant metrics including dynamic overshoots and express saccades. Data from sixty-five human subjects were used to support this experimental study. The performance of the proposed algorithm was tested and compared to manual classification methods resulting in a detection accuracy of up to 72% for several of the oculomotor plant metrics.