Comparative Analysis of KNN, SVM, DT for EOG based Human Computer Interface

In this study, EOG signal based human computer interface (HCI) has been implemented. This is a communication system that permits interaction with computer using eye movement. The necessary steps of implementation of HCI are EOG signal acquisition and analysis, feature extraction and classification. EOG signal has been acquired by placing electrodes at left and right corner of eye from 12 subjects. Dual Tree Complex Wavelet Transform (DTCWT) has been employed to denoise the EOG signal and 16 features are extracted from the time domain. Three classifiers (Decision Tree (DT), k-Nearest Neighbor (KNN) algorithm, and Support Vector Machines (SVM)) have been used to identify the horizontal eye movement i.e. left and right. Analysis and comparison of their performance is made on the basis of confusion matrix, receiver operating characteristics (ROC) and performance indices i.e. sensitivity, specificity, precision, accuracy and F1 score to evaluate the most efficient classifier for their classification task. According to classification results, out of three classifiers, KNN is the best classifier for horizontal EOG signal and has shown almost 100 percent accuracy.