EOG Based Eye Movements and Blinks Classification Using Irisgram and CNN-SVM Classifier

The classification of eye movements and blinks is an important task in various fields, including ophthalmology, psychology, and human-computer interaction. In recent years, the use of EOG signals and convolutional neural networks (CNNs) has shown promising results in accurately classifying different types of eye movements and blinks. The Irisgram, which is a two-dimensional representation of the short-time Fourier transform in the shape of a human iris, has been used as a feature for distinguishing between different types of eye movements and blinks. Additionally, CNNs have been utilized to learn the features automatically from Irisgrams and classify the eye movements and blinks based on these learned features. In this paper, we provide a methodology to classify blinks and four eye movements by employing Irisgram as input to the CNN-SVM classifier which achieved test accuracy of 96.2% in the testing dataset.