Electrooculogram (EOG) based Mouse Cursor Controller Using the Continuous Wavelet Transform and Statistic Features

Abstract

This study design a system prototype to control a mouse cursor's movement on a computer using an electrooculogram (EOG) signal. The EOG signal generated from eye movement was processed utilizing a microcontroller with an analog to the digital conversion process, which communicates with the computer through a USB port. The signal was decomposed using continuous wavelet transform (CWT), followed by feature extraction processes using statistic calculation, and then classified using K-Nearest Neighbors (k-NN) to decide the movement and direction of the mouse cursor. The test was carried out with 110 EOG signals then separated, 0.5 as training data and 0.5 as test data with eight categories of directional movement patterns, including up, bottom, right, left, top right, top left, bottom right bottom left. The highest accuracy that can be achieved using CWT-bump and kurtosis is 100%, while the time needed to translate the eye movement to the cursor movement is 1.9792 seconds. It is hoped that the proposed system can help assistive devices, particularly for Amyotrophic Lateral Sclerosis (ALS) sufferers.