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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2021-08-01
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Online Access: | https://doi.org/10.1038/s41598-021-95275-1 |
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doaj-49cfd8d82d194c6ca096f0c9afd687692021-08-08T11:24:33ZengNature Publishing GroupScientific Reports2045-23222021-08-0111111010.1038/s41598-021-95275-1Eye tracking based dyslexia detection using a holistic approachBoris Nerušil0Jaroslav Polec1Juraj Škunda2Juraj Kačur3Institute of Multimedia ICT, Slovak University of Technology in BratislavaInstitute of Multimedia ICT, Slovak University of Technology in BratislavaInstitute of Multimedia ICT, Slovak University of Technology in BratislavaInstitute of Multimedia ICT, Slovak University of Technology in BratislavaAbstract 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.https://doi.org/10.1038/s41598-021-95275-1 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Boris Nerušil Jaroslav Polec Juraj Škunda Juraj Kačur |
spellingShingle |
Boris Nerušil Jaroslav Polec Juraj Škunda Juraj Kačur Eye tracking based dyslexia detection using a holistic approach Scientific Reports |
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 Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2021-08-01 |
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. |
url |
https://doi.org/10.1038/s41598-021-95275-1 |
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