Abstract
| 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 |
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A simple algorithm for the offline recalibration of eye-tracking data through best-fitting linear transformation. / Vadillo, Miguel A.; Street, Chris; Beesley, Tom; Shanks, David R.
In: Behavior Research Methods, Vol. 47, No. 4, 12.2015, p. 1365-1376.Research output: Contribution to journal › Article
TY - JOUR
T1 - A simple algorithm for the offline recalibration of eye-tracking data through best-fitting linear transformation
AU - Vadillo, Miguel A.
AU - Street, Chris
AU - Beesley, Tom
AU - Shanks, David R.
PY - 2015/12
Y1 - 2015/12
N2 - 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.
AB - 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.
KW - Drift correction
KW - Eye-tracking
KW - Recalibration
UR - https://link.springer.com/journal/13428
U2 - 10.3758/s13428-014-0544-1
DO - 10.3758/s13428-014-0544-1
M3 - Article
VL - 47
SP - 1365
EP - 1376
JO - Behavior Research Methods
JF - Behavior Research Methods
SN - 1554-351X
IS - 4
ER -