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dc.contributor.authorKomogortsev, Oleg V.
dc.date.accessioned2012-06-10T22:36:05Z
dc.date.available2012-06-10T22:36:05Z
dc.date.issued2012-06-10
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/4157
dc.description.abstractComplex 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.en_US
dc.description.sponsorshipNational Institute of Standards and Technology (NIST) under Grant #60NANB10D213 National Science Foundation (NSF) under Grant #DGE-11444666en_US
dc.formatText
dc.format.extent31 pages
dc.format.medium1 file (.pdf)
dc.language.isoen_US
dc.relation.ispartofseries;TR2012_06_10_COB_Ko_Da_Go
dc.subjectClassificationen_US
dc.subjectAlgorithmen_US
dc.subjectSaccadeen_US
dc.subjectOculomotor behavioren_US
dc.titleAutomated Classification of Complex Oculomotor Behavioren_US
txstate.publication.titleAutomated Classification of Complex Oculomotor Behavioren_US
txstate.labelComplex Oculomotor Behavioren_US
txstate.documenttypeTechnical Reporten_US
txstate.departmentComputer Science


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