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Projects -> Eye Movement Classification

 

 

Goal: Accurately classify various types of the eye movement behavior offline and in the real-time from the raw eye positional signal. This classification includes basic eye movements such as fixations, saccades, smooth pursuits and complex eye movement behavior such as simple and corrective overshoots/undershoots, dynamic, express, compound saccades, and others.

Motivation: Eye tracking field is rapidly growing because the information extracted from the recorded eye movements can be employed in any field that uses the information related to the human vision. An eye tracker records eye movements in the raw positional form and it is necessary to extract basic eye movements from that raw signal to interpret Human Visual System (HVS) behavior. This research direction investigates robust and efficient algorithms for eye movement classification and develops simple to use mechanisms that enable assessing the meaningfulness of the classification performance.

Project Status: Looking for students interested in the project. Please contact Dr. Komogortsev if interested in participation.

Publications:

O. V. Komogortsev, A. Karpov, Automated Classification and Scoring of Smooth Pursuit Eye Movements in Presence of Fixations and Saccades, Journal of Behavioral Research Methods, pp. 1-13, 2012. [.pdf]

O. V. Komogortsev, D. V. Gobert, S. Jayarathna, D. Koh, & S. Gowda, Standardization of Automated Analyses of Oculomotor Fixation and Saccadic Behaviors. IEEE Transactions on Biomedical Engineering, 57 (11), pp. 2635-2645, 2010. [.pdf][project's software].

M. Gowda, and O. V. Komogortsev, Real Time Eye Movement Identification Protocol. In Proceedings of ACM Conference on Human Factors in Computing Systems (CHI), Atlanta, GA, 2010, pp. 1-6.[.pdf].

O. V. Komogortsev, U. K. S. Jayarathna, D. H. Koh, and M. Gowda, Qualitative and Quantitative Scoring and Evaluation of the Eye Movement Classification Algorithms. In Proceedings of ACM Eye Tracking Research & Applications Symposium, Austin, TX, 2010, pp. 1-4. [.pdf]. [detailed technical report][project's software]

 Koh, D., Gowda, S., Komogortsev, O. Input Evaluation of an Eye-Gaze-Guided Interface: Kalman Filter vs. Velocity Threshold Eye Movement Identification. In Proceedings of of the ACM SIGCHI symposium on engineering interactive computing systems (EICS 2009), July 2009. [.pdf]

Technical Reports:

O. V. Komogortsev, Z. Dai, and D. Gobert, Automated Classification of Complex Oculomotor Behavior, Texas State University, Technical Report, 2012, https://digital.library.txstate.edu/handle/10877/4157

O. V. Komogortsev, U. K. S. Jayarathna, D. H. Koh, and S. M. Gowda, Qualitative and Quantitative Scoring and Evaluation of the Eye Movement Classification Algorithms, Texas State University, Technical Report, 2009, https://digital.library.txstate.edu/handle/10877/2577

Software:

[link]

 
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