A simple algorithm for the offline recalibration of eye-tracking data through best-fitting linear transformation.
A simple algorithm for the offline recalibration of eye-tracking data through best-fitting linear transformation.
复制标题
DOI：
10.3758/s13428-014-0544-1
复制
发表时间：
2015-12
影响因子：
5.4
通讯作者：
Shanks DR
中科院分区：
文献类型：
--
作者：
Vadillo MA;Street CNH;Beesley T;Shanks DR
不好的校准和不准确的漂移校正可能会给要求高精度和精密度的眼睛跟踪实验带来严重的问题。描述了一种眼动跟踪数据的离线校正算法。该算法对注视的坐标进行线性变换，以最小化每个注视与其最接近的刺激之间的距离。文中还给出了一个简单的MatLab实现方法。我们使用模拟数据和真实数据考察了几种情况下的校正算法的性能，并表明当拟合过程中包含多个固定时，该算法特别有可能提高数据质量。
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.