Assessment of dyslexic children with EOG signals: Determining retrieving words/re-reading and skipping lines using convolutional neural networks

This study aims to determine and classify the back to eye movement (retrieving words/re-reading) and skipping lines while reading from electrooculography (EOG) signals. For this aim, EOG signals were recorded during the reading of a text from healthy and from dyslexic children. In this study, a method to assist in the diagnosis and follow-up of dyslexia is proposed by determining skipping lines and back to eye movement (retrieving words/re-reading) while reading. Using the proposed method, skipping lines while reading and back to eye movement (retrieving words/re-reading movements) were determined from EOG signals and spectrogram images of these movement signals are obtained using the Short Time Fourier Transform (STFT) method. These spectrogram images were classified using the 2 Dimensional Convolutional Neural Network (2D-CNN) classifier. The 2D-CNN model has classified the skipping lines signals while reading and back to eye movement (retrieving words/re-reading) signals with 99% success. The findings show that the method proposed in the diagnosis and follow-up of dyslexia can give positive results using these EOG signals. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This study proposes a method to assist in diagnosing and following up dyslexia by analyzing skipping lines and back to eye movements while reading from electrooculography signals. The 2D-CNN classifier achieved a success rate of 99% in classifying these movement signals.