# Evaluating Eye Tracking Signal Quality with Real-time Gaze Interaction Simulation

arXiv: https://arxiv.org/abs/2411.03708 · HTML: https://mlsanity.com/paper/2411.03708/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2024.

## Built on by (most cited first, 4 of 4)

- [Ocular Authentication: Fusion of Gaze and Periocular Modalities](https://mlsanity.com/paper/2505.17343/) (2025)
- [Gaze Authentication: Factors Influencing Authentication Performance](https://mlsanity.com/paper/2509.10969/) (2025)
- [Neuromorphic Eye Tracking for Low-Latency Pupil Detection](https://mlsanity.com/paper/2512.09969/) (2025)
- [Enhancing Eye Movement Biometrics for User Authentication via Continuous Gaze Offset Score Fusion](https://mlsanity.com/paper/2605.06810/) (2026)

## Abstract (arXiv)

We present a real-time gaze-based interaction simulation methodology using an offline dataset to evaluate the eye-tracking signal quality. This study employs three fundamental eye-movement classification algorithms to identify physiological fixations from the eye-tracking data. We introduce the Rank-1 fixation selection approach to identify the most stable fixation period nearest to a target, referred to as the trigger-event. Our evaluation explores how varying constraints impact the definition of trigger-events and evaluates the eye-tracking signal quality of defined trigger-events. Results show that while the dispersion threshold-based algorithm identifies trigger-events more accurately, the Kalman filter-based classification algorithm performs better in eye-tracking signal quality, as demonstrated through a user-centric quality assessment using user- and error-percentile tiers. Despite median user-level performance showing minor differences across algorithms, significant variability in signal quality across participants highlights the importance of algorithm selection to ensure system reliability.