A Study on the Extraction and Analysis of a Large Set of Eye Movement Features during Reading

TL;DR

This paper introduces a comprehensive framework for extracting and analyzing 101 eye movement features during reading, providing insights into their variability and reliability across a large population, with applications in multiple research fields.

Contribution

It presents a unified mathematical framework for extracting a large set of eye movement features and evaluates their reliability in a normative population, advancing eye movement analysis methods.

Findings

Demonstrated the variability of eye movement features across individuals.

Quantified the test-retest reliability of each feature.

Provided a detailed mathematical framework for feature extraction.

Abstract

This work presents a study on the extraction and analysis of a set of 101 categories of eye movement features from three types of eye movement events: fixations, saccades, and post-saccadic oscillations. The eye movements were recorded during a reading task. For the categories of features with multiple instances in a recording we extract corresponding feature subtypes by calculating descriptive statistics on the distributions of these instances. A unified framework of detailed descriptions and mathematical formulas are provided for the extraction of the feature set. The analysis of feature values is performed using a large database of eye movement recordings from a normative population of 298 subjects. We demonstrate the central tendency and overall variability of feature values over the experimental population, and more importantly, we quantify the test-retest reliability (repeatability) of each separate feature. The described methods and analysis can provide valuable tools in fields exploring the eye movements, such as in behavioral studies, attention and cognition research, medical research, biometric recognition, and human-computer interaction.