Tools for eye-tracking data analysis are as of now either provided as proprietary software by the eye-tracker manufacturer or published by researchers under licenses that are problematic for some use-cases (e.g., GPL3). This lead to repeated re-implementation of the most basic building blocks, such as event filters, often resulting in incomplete, incomparable and even erroneous implementations. The Perception Engineer’s Toolkit is a collection of basic functionality for eye-tracking data analysis double licensed with CC0 or MIT license that allows for easy integration, modification and extension of the codebase. Methods for data import from different formats, signal pre-processing and quality checking as well as several event detection algorithms are included. The processed data can be visualized as gaze density map or reduced to key metrics of the detected eye movement events. It is programmed entirely in python utilizing high performance matrix libraries and allows for easy scripting access to batch-process large amounts of data. The code is available at https://bitbucket.org/fahrensiesicher/perceptionengineerstoolkit
{"title":"The Perception Engineer's Toolkit for Eye-Tracking data analysis","authors":"Thomas C. Kübler","doi":"10.1145/3379156.3391366","DOIUrl":"https://doi.org/10.1145/3379156.3391366","url":null,"abstract":"Tools for eye-tracking data analysis are as of now either provided as proprietary software by the eye-tracker manufacturer or published by researchers under licenses that are problematic for some use-cases (e.g., GPL3). This lead to repeated re-implementation of the most basic building blocks, such as event filters, often resulting in incomplete, incomparable and even erroneous implementations. The Perception Engineer’s Toolkit is a collection of basic functionality for eye-tracking data analysis double licensed with CC0 or MIT license that allows for easy integration, modification and extension of the codebase. Methods for data import from different formats, signal pre-processing and quality checking as well as several event detection algorithms are included. The processed data can be visualized as gaze density map or reduced to key metrics of the detected eye movement events. It is programmed entirely in python utilizing high performance matrix libraries and allows for easy scripting access to batch-process large amounts of data. The code is available at https://bitbucket.org/fahrensiesicher/perceptionengineerstoolkit","PeriodicalId":222437,"journal":{"name":"ETRA Short Papers","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124323456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To analyze eye-tracking data the viewed image is often divided into areas of interest (AOI). However, the temporal dynamics of eye movements towards the AOI is often lost either in favor of summary statistics (e.g., proportion of fixations or dwell time) or is significantly reduced by “binning” the data and computing the same summary statistic over each time bin. This paper introduces SPLOT: smoothed proportion of looks over time method for analyzing the eye movement dynamics across AOI. SPLOT comprises of a complete workflow, from visualization of the time-course to performing statistical analysis on it using cluster-based permutation testing. The possibilities of SPLOT are illustrated by applying it to an existing dataset of eye movements of radiologists diagnosing a chest X-ray.
{"title":"Getting more out of Area of Interest (AOI) analysis with SPLOT","authors":"A. Belopolsky","doi":"10.1145/3379156.3391372","DOIUrl":"https://doi.org/10.1145/3379156.3391372","url":null,"abstract":"To analyze eye-tracking data the viewed image is often divided into areas of interest (AOI). However, the temporal dynamics of eye movements towards the AOI is often lost either in favor of summary statistics (e.g., proportion of fixations or dwell time) or is significantly reduced by “binning” the data and computing the same summary statistic over each time bin. This paper introduces SPLOT: smoothed proportion of looks over time method for analyzing the eye movement dynamics across AOI. SPLOT comprises of a complete workflow, from visualization of the time-course to performing statistical analysis on it using cluster-based permutation testing. The possibilities of SPLOT are illustrated by applying it to an existing dataset of eye movements of radiologists diagnosing a chest X-ray.","PeriodicalId":222437,"journal":{"name":"ETRA Short Papers","volume":"2010 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114914388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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