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大量眼动特征研究:提取方法与统计分析

Study of an Extensive Set of Eye Movement Features: Extraction Methods and Statistical Analysis.

作者信息

Rigas Ioannis, Friedman Lee, Komogortsev Oleg

机构信息

Texas State University, San Marcos,, USA.

出版信息

J Eye Mov Res. 2018 Mar 20;11(1). doi: 10.16910/jemr.11.1.3.

Abstract

This work presents a study of an extensive set of 101 categories of eye movement features from three types of eye movement events: fixations, saccades, and post-saccadic oscillations. We present a unified framework of methods for the extraction of features that describe the temporal, positional and dynamic characteristics of eye movements. We perform statistical analysis of feature values by employing eye movement data from a normative population of 298 subjects, recorded during a text reading task. We present overall measures for the central tendency and variability of feature values, and we quantify the test-retest reliability of features using either the Intraclass Correlation Coefficient (for normally distributed and normalized features) or Kendall's coefficient of concordance (for non-normally distributed features). Finally, for the case of normally distributed and normalized features we additionally perform factor analysis and provide interpretations of the resulting factors. The presented methods and analysis can provide a valuable tool for researchers in various fields that explore eye movements, such as in behavioral studies, attention and cognition research, medical research, biometric recognition, and humancomputer interaction.

摘要

这项工作展示了一项对来自三种眼动事件(注视、扫视和扫视后振荡)的101类广泛眼动特征的研究。我们提出了一个统一的方法框架,用于提取描述眼动的时间、位置和动态特征的特征。我们通过使用来自298名受试者的规范群体在文本阅读任务期间记录的眼动数据,对特征值进行统计分析。我们给出了特征值的集中趋势和变异性的总体度量,并使用组内相关系数(用于正态分布和归一化特征)或肯德尔和谐系数(用于非正态分布特征)来量化特征的重测信度。最后,对于正态分布和归一化特征的情况,我们还进行因子分析并给出所得因子的解释。所提出的方法和分析可以为探索眼动的各个领域的研究人员提供一个有价值的工具,例如行为研究、注意力和认知研究、医学研究、生物识别以及人机交互。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9115/7722561/94823a7309f2/jemr-11-01-c-figure-11.jpg

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