An adaptive algorithm for fixation, saccade, and glissade detection in eyetracking data

By Marcus Nyström, Kenneth Holmqvist. Behavior Research Methods, 2010

680 citations recorded by the source corpus.

Subfields: Ophthalmology, Radiology, Nuclear Medicine and Imaging, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/8d4b356db6eaf8bff66de9a3be87e5f0ed6da67e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ophthalmology

1. James M. Rehg
2. Sebastiaan Mathôt
3. S. van der Stigchel
4. Yin Li
5. Edwin S. Dalmaijer
6. Alireza Fathi

In Radiology, Nuclear Medicine and Imaging

1. James M. Rehg
2. Max A. Viergever
3. Sebastiaan Mathôt
4. S. van der Stigchel
5. Yin Li
6. Edwin S. Dalmaijer
7. Alireza Fathi

In Human-Computer Interaction

1. James M. Rehg
2. Yin Li
3. Alireza Fathi
4. Sebastiaan Mathôt
5. S. van der Stigchel
6. Edwin S. Dalmaijer
7. Kristen Grauman
8. Giovanni Maria Farinella

Citations in the corpus

• Do you like what you see? The role of first fixation and total fixation duration in consumer choice (2015, background): “Adaptive velocity threshold methods are well acknowledged and commonly used to define fixations (Engbert & Kliegl, 2003; Nyström & Holmqvist, 2010; Smeets & Hooge, 2003).”
• Learning to Predict Gaze in Egocentric Video (2013, methodology): “5− 1 degree) for a minimum period of time (80 − 120ms) [18].”
• PyGaze: An open-source, cross-platform toolbox for minimal-effort programming of eyetracking experiments (2014, methodology): “Therefore, researchers are advised to analyze the raw eye-movement data using an algorithm that is designed for offline event detection, e.g. Engbert & Kliegl (2003) or Nyström & Holmqvist (2010), as one would normally do when analyzing eye-movement data.”