Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - A simple algorithm for the offline recalibration of eye-tracking data through best-fitting linear transformation
AU - Vadillo, M.A.
AU - Street, C.N.H.
AU - Beesley, T.
AU - Shanks, D.R.
N1 - cited By 2
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
U2 - 10.3758/s13428-014-0544-1
DO - 10.3758/s13428-014-0544-1
M3 - Journal article
VL - 47
SP - 1365
EP - 1376
JO - Behavior Research Methods
JF - Behavior Research Methods
SN - 1554-351X
IS - 4
ER -