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 -