CHAP: An Open Source Software for Processing and Analyzing Pupillometry Data Ronen Hershman1,2, Noga Cohen3 and Avishai Henik2,4 1Department
of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel 3Department of Psychology, Columbia University, New York, USA 4Department of Psychology, Ben-Gurion University of the Negev, Beer-Sheva, Israel
2Zlotowski
Introduction Pupil dilation is an effective indicator of cognitive load [1]. There are many available eye tracker systems in the market that provide effective solutions for pupil dilation measurement, which can be used to assess different cognitive and affective processes. However, there is a lack of tools for processing and analyzing the data provided by these systems. For this reason, we developed CHAP an open source software written in Matlab.
nput Our software receives input of a standard output file of the Eyelink eye tracker (EDF file) or Eye-tribe eye tracker (CSV file). For processing the Eyelink files, we use EDFMEX software that uses the Eyelink EDF access application programming interface (API) to read Eyelink data files into a Matlab structure. With this method we avoid unnecessary converting to a text file and can save time analyzing the results for each participant (e.g., a 40-minute recording will take about one minute).
Figure 1: Pupil size (in pixels) as a function of time (ms). Red line - before blinks correction fixed. Black line - after blinks. correction.
Our software lets the user decide: •Whether to remove outlier samples by Z scores (we recommend using 4 Z scores). •Whether to remove outlier trials based on the percentage number of missing pupil values (we recommend using 50 percent). •Whether to remove subjects based on a user-defined number of missing trials.
Time-Course Visualization The graph of one participant that presents the average pupil size in each of the conditions:
Data Processing First, the pupil data is extracted from the input file (pupil size in pixels / arbitrary units). Then the software excludes outlier samples (by a selected Z score) and trials with more than the allowable selected percentages of missing pupil values. After that, the software looks for blinks in the converted data (Matlab’s matrix) and reconstructs the pupil size from the relevant samples using cubic-spline interpolation. For this purpose, we use Mathôt’s [2] algorithm for each trial. To find blinks onset and offset, we use a unique algorithm that takes into account the change in pupil size between subsequent samples.
Figure 2: Pupil size (in pixels) as a function of time (ms). Each curve represents a different condition. The vertical lines represent average time for the relevant condition.
The software presents behavioral data and the number of trials that were used (and outliers) in a dedicated table. Each row represents a different condition:
Variables & Events Our software uses user-defined variables and events. These variables and events help researchers get better insights about the trials. These objects are defined by the researcher when building his/her experiment and are sent to the eye tracker during the study. The variables are sent at the moment each trial ends and the events are sent at the moment each event occurs. Our software supports a huge number of variables and events that the user can create (up to 64 for each).
Advanced Processing Options The software supports several simple and powerful features for analyzing: Bins: Averaging of X (user-defined) adjacent samples. Range: The user can select a specific timewindow (from one event to another) for visualization analyses purposes. Pre-event: The user can add pre-event samples to the time-course for visualization and analyses purposes. Relative Changes: The user can present the data by relative changes ( 𝑟𝑒𝑙𝑎𝑡𝑖𝑣𝑒 = 𝑣𝑎𝑙𝑢𝑒 − 𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒) or present the relative change by percentages ( 𝑟𝑒𝑙𝑎𝑡𝑖𝑣𝑒% = 100 ⋅ 𝑟𝑒𝑙𝑎𝑡𝑖𝑣𝑒 /𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒). The baseline is an average of 200 milliseconds prior to the onset of the first event.
In addition, the user can easily convert pixel data to mm by recording an artificial pupil of fixed size and providing the software with the mm and pixel size of it. Our software uses a simple equation to get the sizes in mm: 𝑝𝑢𝑝𝑖𝑙 𝑚𝑚 = 𝑝𝑢𝑝𝑖𝑙 𝑝𝑖𝑥𝑒𝑙𝑠 ⋅ 𝑟𝑎𝑡𝑖𝑜 where 𝑟𝑎𝑡𝑖𝑜 is the ratio between the artificial pupil (ap) in mm and in pixels described by: 𝑟𝑎𝑡𝑖𝑜 =
𝜋 𝑎𝑝[𝑚𝑚] 2 𝑎𝑝[𝑚𝑚] = 𝜋(𝑎𝑝[𝑝𝑖𝑥𝑒𝑙𝑠])2 𝑎𝑝[𝑝𝑖𝑥𝑒𝑙𝑠]
Output Our software supports 3 kind of outputs : 1. CSV / Matlab file that contains all the data about the relevant variables and events for each participant. 2. Matlab figure with the graphs of average data for each participant. 3. PNG image of the graph of the average data for each participant. In addition, our software has an option to run the same configuration for multiple input files (e.g., different participants) and get the same trial data and average data outputs for those files.
Figure 4: Relative pupil size (in Z scores) as a function of time (ms), allocated to bins of 100 milliseconds for 19 participants.
References
Figure 3: Relative pupil size (in Z scores) as a function of time (ms), allocated to bins of 100 milliseconds, displayed from 2 seconds before the first chosen event until the second chosen event.
[1] Beatty, J., & Kahneman, D. (1966). Pupillary changes in two memory tasks. Psychonomic Science, 5(10), 371-372. [2] Mathôt, S. (2013). A simple way to reconstruct pupil size during eye blinks. Available: http://dx.doi.org/10.6084/m9.figshare.688002.
Converting to Z Scores / mm The user can easily convert pixel data to Z scores by using the entire time course (i.e., based on the mean and SD calculated on all valid trials).
For More Information
[email protected] [email protected] in.bgu.ac.il/en/Labs/CNL/chap
