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

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 human-computer interaction

Multimodality during fixation – Part II: Evidence for multimodality in spatial precision-related distributions and impact on precision estimates

This paper is a follow-on to our earlier paper (Friedman, Lohr, Hanson, & Komogortsev, 2021), which focused on the multimodality of angular offsets.  This paper applies the same analysis to the measurement of spatial precision.  Following the literature, we refer these measurements as estimates of device precision, but, in fact, subject characteristics clearly affect the measurements.  One typical measure of the spatial precision of an eye-tracking device is the standard deviation (SD) of the position signals (horizontal and vertical) during a fixation.  The SD is a highly interpretable measure of spread if the underlying error distribution is unimodal and normal. However, in the context of an underlying multimodal distribution, the SD is less interpretable. We will present evidence that the majority of such distributions are multimodal (68-70% strongly multimodal).  Only 21-23% of position distributions were unimodal. We present an alternative method for measuring precision that is appropriate for both unimodal and multimodal distributions.  This alternative method produces precision estimates that are substantially smaller than classic measures.  We present illustrations of both unimodality and multimodality with either drift or a microsaccade present during fixation.  At present, these observations apply only to the EyeLink 1000, and the subjects evaluated herein

An Assessment of the Eye Tracking Signal Quality Captured in the HoloLens 2

We present an analysis of the eye tracking signal quality of the HoloLens 2s integrated eye tracker. Signal quality was measured from eye movement data captured during a random saccades task from a new eye movement dataset collected on 30 healthy adults. We characterize the eye tracking signal quality of the device in terms of spatial accuracy, spatial precision, temporal precision, linearity, and crosstalk. Most notably, our evaluation of spatial accuracy reveals that the eye movement data in our dataset appears to be uncalibrated. Recalibrating the data using a subset of our dataset task produces notably better eye tracking signal quality.Comment: 10 pages, 10 figure