Automated Classification of Complex Oculomotor Behavior
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Complex oculomotor behavior in response to a simple step stimulus can include a variety of different types of saccadic patterns including combinations of normal saccades, simple/corrected/multi-corrected overshoots/undershoots, express, dynamic overshoots, and compound saccades depending on the state of the oculomotor plant and the neuronal control signal supplied by the brain. This paper presents an algorithmic framework that allows automated classification of such behavior. Automated classification results were compared to manually classified data used as a reference baseline. In addition, this work investigates the impact of various filtering methods and basic eye movement classification algorithms on the accuracy of classification of complex oculomotor behavior. The proposed framework can be used in clinical examination of normal and abnormal visual systems.