Abstract
Poor calibration and inaccurate drift correction can pose severe problems for eye-tracking experiments requiring high levels of accuracy and precision. We describe an algorithm for the offline correction of eye-tracking data. The algorithm conducts a linear transformation of the coordinates of fixations that minimizes the distance between each fixation and its closest stimulus. A simple implementation in MATLAB is also presented. We explore the performance of the correction algorithm under several conditions using simulated and real data, and show that it is particularly likely to improve data quality when many fixations are included in the fitting process.
| Original language | English |
|---|---|
| Pages (from-to) | 1365-1376 |
| Number of pages | 12 |
| Journal | Behavior Research Methods |
| Volume | 47 |
| Issue number | 4 |
| Early online date | 1 Jan 2015 |
| DOIs | |
| Publication status | Published - Dec 2015 |
| Externally published | Yes |
Fingerprint Dive into the research topics of 'A simple algorithm for the offline recalibration of eye-tracking data through best-fitting linear transformation'. Together they form a unique fingerprint.
Profiles
Christopher Street
- Department of Psychology - Reader
- School of Human and Health Sciences
- The Centre for Cognition and Neuroscience - Co-Director
- Secure Societies Institute - Member
- Centre for Applied Psychological Research - Associate Member
Person: Academic
Cite this
Vadillo, M. A., Street, C., Beesley, T., & Shanks, D. R. (2015). A simple algorithm for the offline recalibration of eye-tracking data through best-fitting linear transformation. Behavior Research Methods, 47(4), 1365-1376. https://doi.org/10.3758/s13428-014-0544-1