EOG Signal Classification with Wavelet and Supervised Learning Algorithms KNN, SVM and DT

Abstract

The work carried out in this paper consists of the classification of the physiological signal generated by eye movement called Electrooculography (EOG). The human eye performs simultaneous movements, when focusing on an object, generating a potential change in origin between the retinal epithelium and the cornea and modeling the eyeball as a dipole with a positive and negative hemisphere. Supervised learning algorithms were implemented to classify five eye movements; left, right, down, up and blink. Wavelet Transform was used to obtain information in the frequency domain characterizing the EOG signal with a bandwidth of 0.5 to 50 Hz; training results were obtained with the implementation of K-Nearest Neighbor (KNN) 69.4%, a Support Vector Machine (SVM) of 76.9% and Decision Tree (DT) 60.5%, checking the accuracy through the Jaccard index and other metrics such as the confusion matrix and ROC (Receiver Operating Characteristic) curve. As a result, the best classifier for this application was the SVM with Jaccard Index.

Application Specific Reconfigurable Processor for Eyeblink Detection from Dual-Channel EOG Signal

“…Paralyzed people are unable to move the affected parts of the body either partly or entirely. They face difficulties in performing routine activities and communicating with the external world, which impacts their quality of life [3]. Therefore, scientific research is being conducted to ease the lives of these partially or fully disabled people by creating an alternative form of communication.…”