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

Abstract 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.

[1]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[2]  Learning disorders in children and adolescents. , 2007, Primary care.

[3]  B. Fischer,et al.  Saccadic Eye Movements of Dyslexic and Normal Reading Children , 1994, Perception.

[4]  Desney S. Tan,et al.  Brain-Computer Interfacing for Intelligent Systems , 2008, IEEE Intelligent Systems.

[5]  Huiping Jiang,et al.  Classification of EEG Signal by STFT-CNN Framework: Identification of Right-/left-hand Motor Imagination in BCI Systems , 2017 .

[6]  K. Rayner,et al.  Mindless reading revisited: Eye movements during reading and scanning are different , 1996, Perception & psychophysics.

[7]  G. Pavlidis,et al.  Eye Movements in Dyslexia , 1985, Journal of learning disabilities.

[8]  Mahmut Ozer,et al.  Detection of directional eye movements based on the electrooculogram signals through an artificial neural network , 2015 .

[9]  P. Svenningsson,et al.  Eye movements during reading in Parkinson's disease: A pilot study , 2018, Movement disorders : official journal of the Movement Disorder Society.

[10]  K. Stanovich,et al.  What Reading Does for the Mind. , 1998 .

[11]  S. Valdois,et al.  The eye movements of dyslexic children during reading and visual search: Impact of the visual attention span , 2007, Vision Research.

[12]  Nikhil Ketkar,et al.  Convolutional Neural Networks , 2021, Deep Learning with Python.

[13]  U. Rajendra Acharya,et al.  Arrhythmia detection using deep convolutional neural network with long duration ECG signals , 2018, Comput. Biol. Medicine.

[14]  Octavian Postolache,et al.  An Affordable Method for Evaluation of Ataxic Disorders Based on Electrooculography , 2019, Sensors.

[15]  J. Stein,et al.  Differences in eye movements and reading problems in dyslexic and normal children , 1994, Vision Research.

[16]  Bin Yao,et al.  ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network , 2019, IEEE Access.

[17]  K. Rayner,et al.  Toward a model of eye movement control in reading. , 1998 .

[18]  S Ramkumar,et al.  Design and Development of Human Computer Interface Using Electrooculogram with Deep Learning , 2020, Artif. Intell. Medicine.

[19]  Jiann-Shiun Yuan,et al.  ECG Arrhythmia Classification Using Transfer Learning from 2- Dimensional Deep CNN Features , 2018, 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[20]  G. Arbanas.  Diagnostic and Statistical Manual of Mental Disorders (DSM-5) , 2015 .

[21]  Chin-Teng Lin,et al.  EOG-Based Eye Movement Classification and Application on HCI Baseball Game , 2019, IEEE Access.

[22]  Fatma Latifoğlu,et al.  Detection of Reading Movement from EOG Signals , 2020, 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[23]  Amit Konar,et al.  Eye movement sequence analysis using electrooculogram to assist autistic children , 2014, Biomed. Signal Process. Control..

[24]  K. Rayner.  Eye movements in reading and information processing: 20 years of research. , 1998, Psychological bulletin.

[25]  Amit Konar,et al.  Classifying Electrooculogram to Detect Directional Eye Movements , 2013 .

[26]  Kwang Suk Park,et al.  A Novel Wearable Forehead EOG Measurement System for Human Computer Interfaces , 2017, Sensors.

[27]  Mary F. Bond,et al.  Fractal Analysis of Eye Movements during Reading , 2001, Optometry and vision science : official publication of the American Academy of Optometry.

[28]  U. Rajendra Acharya,et al.  Application of deep transfer learning for automated brain abnormality classification using MR images , 2019, Cognitive Systems Research.