Texas State University
eCommons@Texas State University Technical Reports-Computer Science
Computer Science, Department of
5-4-2010
Classification Algorithm for Saccadic Oculomotor Behavior Oleg Vladimirovich Komogortsev Texas State University-San Marcos, Dept. of Computer Science,
[email protected]
Denise Gobert Texas State University-San Marcos, Dept. of Physical Therapy,
[email protected]
Zanxun Dai Texas State University-San Marcos, Dept. of Computer Scienc,
[email protected]
Recommended Citation Komogortsev, Oleg Vladimirovich; Gobert, Denise; and Dai, Zanxun, "Classification Algorithm for Saccadic Oculomotor Behavior" (2010). Technical Reports-Computer Science. Paper 18. http://ecommons.txstate.edu/cscitrep/18
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Department of Computer Science San Marcos, TX 78666 Report Number TXSTATE-CS-TR-2010-23 Classification Algorithm for Saccadic Oculomotor Behavior Oleg V. Komogortsev Denise Gobert Zanxun Dai
2010-05-04
Classification Algorithm for Saccadic Oculomotor Behavior Oleg V. Komogortsev Department of Computer Science Texas State University-San Marcos
[email protected]
Denise Gobert Department of Physical Therapy Texas State University-San Marcos
[email protected]
A bstract
attentional deficit disorders [5], and persons suffering from
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
Zanxun Dai Department of Computer Science Texas State University-San Marcos
[email protected]
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
vestibular-UHODWHGSDWKRORJLHVOLNH0HQLHUH¶V'LVHDVH [6]. Tracking changes in eye movement control can provide information about patient responses to medication or improvements in functional tasks during activities of daily living such as reading [4]. During recent years, eye movement classification algorithms have been increasingly used in the oculomotor field to aid our understanding of normal eye function control in response to external stimuli or due to pathology or aging [7]. However, to the best of our knowledge an automated classification algorithm for the assessment of oculomotor behavior during saccades does not exist. In this paper we describe development of such an algorithm and report the performance of the algorithm versus manually classified data.
2
O culomotor Behavior During Saccades
oculomotor plant metrics.
The oculomotor behavior during saccades can be
C R C ategories: I.6.4 [Simulation and Modeling]: Model
accessed
via the amount
and
magnitude of hypometria
Validation and Analysis; J.7 [Computers in Other Systems]:
(undershoot)/hypermetria (overshoot), number of express saccades
Process control, Real time.
and dynamic overshoots [8]. A practical look at the hypometric
K eywords: classification, algorithm, saccade, oculomotor
and hypermetric behavior of the oculomotor plant allows assessment of such behavior through a number of simplified cases
behavior.
such
1
Introduction
as
simple
undershoot,
simple
overshoot,
corrected
undershoot, corrected overshoot, multi corrected undershoot, multi
The assessment of oculomotor behavior is fundamental to
corrected overshoot, and compound saccade. Figure 1 presents
clinical examination of visual system pathology. Two primary eye
eight specific examples with descriptions of each case provided in
movements, fixation and saccadic function, have proven valuable
the text below. Stimulus saccade is defined as a step in the
in the diagnosis of several psychological, degenerative and
stimulus positional signal.
neurological disorders.
Simple Undershoot/O vershoot: the offset position of the stimulus
For instance, abnormal saccadic eye
EHKDYLRU LV FRPPRQ LQ SDWLHQWV GLDJQRVHG ZLWK $O]KHLPHU¶V disease [1], schizophrenia [2], macular degeneration [3-4],
induced saccade or eye movement falls below/above a certain threshold (>0.5q in our work) from stimulus fixation position.
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45%/"63.++&3/&*'45%/"63.++&3/&*' 9:);#"3' 01&+,-../ 01&+,-../ ()*&+,-../
F igure 1. Examples of Oculomotor Plant metrics with eye positional accuracy better than 0.5º.
78$+&,,'
0.5q but 0.5q in our work) from stimulus fixation position. The corrective Definitions provided in the section above seem to be saccade following the initial undershoot/overshoot results in an eye theoretically simple and straightforward behaviors to classify. The fixation position within a certain threshold of the fixation stimulus practical challenges come from the fact that the eye position signal ( last_saccade.offset_position_deg Note: SCC e and SCC f are sign sensitive
F igure 2. Deterministic Finite Automation of Saccadic Oculomotor Behavior
the point remained stationary for 1.5s before the next jump was initiated. The
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