Eye tracking based dyslexia detection using a holistic approach

• Published in: Scientific reports 2021-08, Vol.11 (1), p.15687-15687, Article 15687
• Main Authors: Nerušil, Boris, Polec, Jaroslav, Škunda, Juraj, Kačur, Juraj
• Format: Article
• Language: English
• Subjects: 639/166/987; 639/705/1046; 639/705/117; 692/308/2778; Cognitive ability; Dyslexia; Eye movements; Humanities and Social Sciences; Information processing; Learning algorithms; Machine learning; multidisciplinary; Neural networks; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-95275-1

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.