MAD saccade: statistically robust saccade threshold estimation via the median absolute deviation

Saccade detection is a critical step in the analysis of gaze data. A common method for saccade detection is to use a simple threshold for velocity or acceleration values, which can be estimated from the data using the mean and standard deviation. However, this method has the downside of being influe...

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Main Authors: Benjamin Voloh, Marcus R Watson, Seth Konig, Thilo Womelsdorf
Format: Article
Language:English
Published: MDPI AG 2020-05-01
Series:Journal of Eye Movement Research
Subjects:
Online Access:https://bop.unibe.ch/JEMR/article/view/5992
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author Benjamin Voloh
Marcus R Watson
Seth Konig
Thilo Womelsdorf
author_facet Benjamin Voloh
Marcus R Watson
Seth Konig
Thilo Womelsdorf
author_sort Benjamin Voloh
collection DOAJ
description Saccade detection is a critical step in the analysis of gaze data. A common method for saccade detection is to use a simple threshold for velocity or acceleration values, which can be estimated from the data using the mean and standard deviation. However, this method has the downside of being influenced by the very signal it is trying to detect, the outlying velocities or accelerations that occur during saccades. We propose instead to use the median absolute deviation (MAD), a robust estimator of dispersion that is not influenced by outliers. We modify an algorithm proposed by Nyström and colleagues, and quantify saccade detection performance in both simulated and human data. Our modified algorithm shows a significant and marked improvement in saccade detection - showing both more true positives and less false negatives – especially under higher noise levels. We conclude that robust estimators can be widely adopted in other common, automatic gaze classification algorithms due to their ease of implementation.
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issn 1995-8692
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publisher MDPI AG
record_format Article
series Journal of Eye Movement Research
spelling doaj-art-211d9d284a37459cb63bb3cbf1d7b5112025-08-20T02:15:33ZengMDPI AGJournal of Eye Movement Research1995-86922020-05-0112810.16910/jemr.12.8.3MAD saccade: statistically robust saccade threshold estimation via the median absolute deviationBenjamin Voloh0Marcus R Watson1Seth Konig2Thilo Womelsdorf3Vanderbilt UniversityYork UniversityVanderbilt UniversityVanderbilt UniversitySaccade detection is a critical step in the analysis of gaze data. A common method for saccade detection is to use a simple threshold for velocity or acceleration values, which can be estimated from the data using the mean and standard deviation. However, this method has the downside of being influenced by the very signal it is trying to detect, the outlying velocities or accelerations that occur during saccades. We propose instead to use the median absolute deviation (MAD), a robust estimator of dispersion that is not influenced by outliers. We modify an algorithm proposed by Nyström and colleagues, and quantify saccade detection performance in both simulated and human data. Our modified algorithm shows a significant and marked improvement in saccade detection - showing both more true positives and less false negatives – especially under higher noise levels. We conclude that robust estimators can be widely adopted in other common, automatic gaze classification algorithms due to their ease of implementation.https://bop.unibe.ch/JEMR/article/view/5992Saccadesmedian absolute deviationMADeye trackinghead-free viewing
spellingShingle Benjamin Voloh
Marcus R Watson
Seth Konig
Thilo Womelsdorf
MAD saccade: statistically robust saccade threshold estimation via the median absolute deviation
Journal of Eye Movement Research
Saccades
median absolute deviation
MAD
eye tracking
head-free viewing
title MAD saccade: statistically robust saccade threshold estimation via the median absolute deviation
title_full MAD saccade: statistically robust saccade threshold estimation via the median absolute deviation
title_fullStr MAD saccade: statistically robust saccade threshold estimation via the median absolute deviation
title_full_unstemmed MAD saccade: statistically robust saccade threshold estimation via the median absolute deviation
title_short MAD saccade: statistically robust saccade threshold estimation via the median absolute deviation
title_sort mad saccade statistically robust saccade threshold estimation via the median absolute deviation
topic Saccades
median absolute deviation
MAD
eye tracking
head-free viewing
url https://bop.unibe.ch/JEMR/article/view/5992
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AT marcusrwatson madsaccadestatisticallyrobustsaccadethresholdestimationviathemedianabsolutedeviation
AT sethkonig madsaccadestatisticallyrobustsaccadethresholdestimationviathemedianabsolutedeviation
AT thilowomelsdorf madsaccadestatisticallyrobustsaccadethresholdestimationviathemedianabsolutedeviation