Identification of fixations and saccades in eye-tracking data using adaptive threshold-based method

TL;DR

This paper introduces an adaptive threshold-based method for detecting fixations and saccades in eye-tracking data, improving robustness to noise and variability across tasks and individuals.

Contribution

The authors propose a novel adaptive thresholding approach using a Markovian model, enhancing detection accuracy and noise robustness over traditional fixed-threshold methods.

Findings

Velocity threshold achieves 90-93% accuracy in clean data.

Adaptive thresholds significantly improve performance under noisy conditions.

Dispersion-based thresholds maintain high accuracy (>81%) even at high noise levels.

Abstract

Properties of ocular fixations and saccades are highly stochastic during many experimental tasks, and their statistics are often used as proxies for various aspects of cognition. Although distinguishing saccades from fixations is not trivial, experimentalists generally use common ad-hoc thresholds in detection algorithms. This neglects inter-task and inter-individual variability in oculomotor dynamics, and potentially biases the resulting statistics. In this article, we introduce and evaluate an adaptive method based on a Markovian approximation of eye-gaze dynamics, using saccades and fixations as states such that the optimal threshold minimizes state transitions. Applying this to three common threshold-based algorithms (velocity, angular velocity, and dispersion), we evaluate the overall accuracy against a multi-threshold benchmark as well as robustness to noise. We find that a velocity threshold achieves the highest baseline accuracy (90-93\%) across both free-viewing and visual search tasks. However, velocity-based methods degrade rapidly under noise when thresholds remain fixed, with accuracy falling below 20% at high noise levels. Adaptive threshold optimization via K-ratio minimization substantially improves performance under noisy conditions for all algorithms. Adaptive dispersion thresholds demonstrate superior noise robustness, maintaining accuracy above 81% even at extreme noise levels ({\sigma} = 50 px), though a precision-recall trade-off emerges that favors fixation detection at the expense of saccade identification. In addition to demonstrating our parsimonious adaptive thresholding method, these findings provide practical guidance for selecting and tuning classification algorithms based on data quality and analytical priorities.