Detection of Reading Movement from EOG Signals

• Published in: 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA) pp. 1 - 5
• Main Authors: Latifoglu, Fatma, Ileri, Ramis, Demirci, Esra, Altintop, Cigdem Guluzar
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-06-2020
• Subjects: Back; biomedical signal processing; classification; Electrooculography; eye movements; Feature extraction; Fluctuations; Human computer interaction; Sensitivity; Tracking
• DOI: 10.1109/MeMeA49120.2020.9137290

In this paper, it is aimed to analysis of Electrooculography (EOG) signals recorded during the back to eye movement (retrieving words/re-reading) and skipping lines while reading. Two situations are characterized by large amplitude fluctuations in EOG signals. For this aim, EOG signals were recorded simultaneously while reading a text from 10 volunteers and changes in EOG signals caused by jumping a bottom line and back movements as reading were analyzed. The classification of these signals may allow the development of a new method for early and rapid diagnosis of various reading disorders (for example dyslexia). This study consists of two main processes; feature extraction and classification. Firstly, two features were determined from the recorded EOG signals for determination of retrieving words/re-reading from EOG signal. Then these signals were applied as input to various classifiers. The classifier performances were evaluated by calculating accuracy, sensitivity, specificity, precision and F measure. Overall classification results were obtained with high performance from all classifiers, and the highest accuracy of the classifiers used was 98% for each of the Random Forest and k-NN classifiers. The results show that this proposed method has an important performance for classification of eye movements from EOG signals.