| Title |
A simple algorithm for the offline recalibration of eye-tracking data through best-fitting linear transformation
|
|---|---|
| Published in |
Behavior Research Methods, January 2015
|
| DOI | 10.3758/s13428-014-0544-1 |
| Pubmed ID | |
| Authors |
Miguel A. Vadillo, Chris N. H. Street, Tom Beesley, David R. Shanks |
| Abstract |
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. |
X Demographics
The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 38% |
| Japan | 1 | 13% |
| Spain | 1 | 13% |
| Unknown | 3 | 38% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 63% |
| Scientists | 2 | 25% |
| Practitioners (doctors, other healthcare professionals) | 1 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 2% |
| Unknown | 79 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 20% |
| Researcher | 14 | 17% |
| Student > Master | 9 | 11% |
| Student > Doctoral Student | 7 | 9% |
| Student > Bachelor | 6 | 7% |
| Other | 15 | 19% |
| Unknown | 14 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 29 | 36% |
| Computer Science | 13 | 16% |
| Neuroscience | 8 | 10% |
| Linguistics | 4 | 5% |
| Engineering | 4 | 5% |
| Other | 8 | 10% |
| Unknown | 15 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 September 2016.
All research outputs
#6,312,736
of 25,371,288 outputs
Outputs from Behavior Research Methods
#773
of 2,524 outputs
Outputs of similar age
#76,965
of 359,525 outputs
Outputs of similar age from Behavior Research Methods
#9
of 26 outputs
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We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.