A simple algorithm for the offline recalibration of eye-tracking data through best-fitting linear transformation

Miguel A. Vadillo, Chris Street, Tom Beesley, David R. Shanks

Research output: Contribution to journalArticle

13 Citations (Scopus)


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 languageEnglish
Pages (from-to)1365-1376
Number of pages12
JournalBehavior Research Methods
Issue number4
Early online date1 Jan 2015
Publication statusPublished - Dec 2015
Externally publishedYes


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