Automated Classification of Complex Oculomotor Behavior

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dc.contributor.authorKomogortsev, Oleg V.
dc.contributor.authorDai, Zanxun
dc.contributor.authorGobert, Denise V.
dc.date.accessioned2012-06-10T22:36:05Z
dc.date.available2012-06-10T22:36:05Z
dc.date.issued2012-06-10
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.
dc.description.departmentComputer Science
dc.description.sponsorshipNational Institute of Standards and Technology (NIST) under Grant #60NANB10D213 National Science Foundation (NSF) under Grant #DGE-11444666
dc.formatText
dc.format.extent31 pages
dc.format.medium1 file (.pdf)
dc.identifier.citationKomogortsev, O., Dai, Z., & Gobert, D. (2012). Automated classification of complex oculomotor behavior (Report No. TXSTATE-CS-TR-2012-6). Texas State University-San Marcos, Department of Computer Science.
dc.identifier.urihttps://hdl.handle.net/10877/4157
dc.language.isoen
dc.relationTR2012_06_10_COB_Ko_Da_Go
dc.subjectclassification
dc.subjectalgorithm
dc.subjectsaccade
dc.subjectoculomotor behavior
dc.subjectComputer Science
dc.titleAutomated Classification of Complex Oculomotor Behavior
dc.typeTechnical Report

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