Eye tracking based dyslexia detection using a holistic approach

Abstract A new detection method for cognitive impairments is presented utilizing an eye tracking signals in a text reading test. This research enhances published articles that extract combination of various features. It does so by processing entire eye-tracking records either in time or frequency wh...

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Autores principales:	Boris Nerušil, Jaroslav Polec, Juraj Škunda, Juraj Kačur
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/49cfd8d82d194c6ca096f0c9afd68769
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spelling	oai:doaj.org-article:49cfd8d82d194c6ca096f0c9afd687692021-12-02T16:35:42ZEye tracking based dyslexia detection using a holistic approach10.1038/s41598-021-95275-12045-2322https://doaj.org/article/49cfd8d82d194c6ca096f0c9afd687692021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95275-1https://doaj.org/toc/2045-2322Abstract A new detection method for cognitive impairments is presented utilizing an eye tracking signals in a text reading test. This research enhances published articles that extract combination of various features. It does so by processing entire eye-tracking records either in time or frequency whereas applying only basic signal pre-processing. Such signals were classified as a whole by Convolutional Neural Networks (CNN) that hierarchically extract substantial features scatter either in time or frequency and nonlinearly binds them using machine learning to minimize a detection error. In the experiments we used a 100 fold cross validation and a dataset containing signals of 185 subjects (88 subjects with low risk and 97 subjects with high risk of dyslexia). In a series of experiments it was found that magnitude spectrum based representation of time interpolated eye-tracking signals recorded the best results, i.e. an average accuracy of 96.6% was reached in comparison to 95.6% that is the best published result on the same database. These findings suggest that a holistic approach involving small but complex enough CNNs applied to properly pre-process and expressed signals provides even better results than a combination of meticulously selected well-known features.Boris NerušilJaroslav PolecJuraj ŠkundaJuraj KačurNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Boris Nerušil Jaroslav Polec Juraj Škunda Juraj Kačur Eye tracking based dyslexia detection using a holistic approach
description	Abstract A new detection method for cognitive impairments is presented utilizing an eye tracking signals in a text reading test. This research enhances published articles that extract combination of various features. It does so by processing entire eye-tracking records either in time or frequency whereas applying only basic signal pre-processing. Such signals were classified as a whole by Convolutional Neural Networks (CNN) that hierarchically extract substantial features scatter either in time or frequency and nonlinearly binds them using machine learning to minimize a detection error. In the experiments we used a 100 fold cross validation and a dataset containing signals of 185 subjects (88 subjects with low risk and 97 subjects with high risk of dyslexia). In a series of experiments it was found that magnitude spectrum based representation of time interpolated eye-tracking signals recorded the best results, i.e. an average accuracy of 96.6% was reached in comparison to 95.6% that is the best published result on the same database. These findings suggest that a holistic approach involving small but complex enough CNNs applied to properly pre-process and expressed signals provides even better results than a combination of meticulously selected well-known features.
format	article
author	Boris Nerušil Jaroslav Polec Juraj Škunda Juraj Kačur
author_facet	Boris Nerušil Jaroslav Polec Juraj Škunda Juraj Kačur
author_sort	Boris Nerušil
title	Eye tracking based dyslexia detection using a holistic approach
title_short	Eye tracking based dyslexia detection using a holistic approach
title_full	Eye tracking based dyslexia detection using a holistic approach
title_fullStr	Eye tracking based dyslexia detection using a holistic approach
title_full_unstemmed	Eye tracking based dyslexia detection using a holistic approach
title_sort	eye tracking based dyslexia detection using a holistic approach
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/49cfd8d82d194c6ca096f0c9afd68769
work_keys_str_mv	AT borisnerusil eyetrackingbaseddyslexiadetectionusingaholisticapproach AT jaroslavpolec eyetrackingbaseddyslexiadetectionusingaholisticapproach AT jurajskunda eyetrackingbaseddyslexiadetectionusingaholisticapproach AT jurajkacur eyetrackingbaseddyslexiadetectionusingaholisticapproach
_version_	1718383688038744064

Eye tracking based dyslexia detection using a holistic approach

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