Design and Implementation of an EOG-based Mouse Cursor Control for Application in Human-Computer Interaction

Abstract

Human Computer Interaction (HCI) has turned into an emerging technology due to the advancement in the field artificial intelligence and biomedical engineering. Acquiring different bio-signals such as Electro-oculography (EOG), Electromyography (EMG) and Electroencephalography (EEG) to control external machine or computer is the essence of HCI technology. In this research, we attempt to extract the EOG signal from different ways of eye movements and process it for HCI application. By utilizing Arduino, EOG data can be transmitted to computer and those signal characteristics is analysed through MATLAB. We have designed and implemented hardware and interfaced it with software to control a computer mouse cursor only by eye movement. Certain classification module like Support Vector machine (SVM) and Multilayer Perceptron (MLP) are used to classify different EOG data generated from different eye movement. According to the eye position, cursor automatically moves in that specific direction and PyAutoGUI module is used for this task. Results after experimentations with different subjects to control mouse cursor in real-time show that the average classification accuracy can reach up to 93% across all directions.

Eye Movement Recognition Using Support Vector Machine

“…Ihsan Al-Kabeer, Faisal bin Shaheen and Muhammad Kafiol Al-Islam (2020) developed a system to extract the EOG signal through the different movements of the eyes and use it to control the computer cursor, through the use of machine learning algorithms such as (SVM, MLP) to classify the different patterns resulting from eye movement. The classification accuracy of the system was 80% when using MLP, 93% when using SVM [14].…”

What Is Hidden in Clear Sight and How to Find It—A Survey of the Integration of Artificial Intelligence and Eye Tracking

“…The fifth identified group is research using eye tracking data as a way to interact with a software. It was used as to authenticate user, both by entering the password [74][75][76][77] and using sclera biometrics [78], to detect defined gestures [79][80][81][82], the desired direction of movement [83] or choosing an answer in a questionnaire form [84].…”

Comparison of LSTM And SVM For Classification of Eye Movements in EOG Signals

“…Ihsan Al-Kabeer, Faisal bin Shaheen and Muhammad Kafiol Al-Islam (2020) developed a system to extract the EOG signal through the different movements of the eyes and use it to control the computer cursor, through the use of machine learning algorithms such as (SVM, MLP) to classify the different patterns resulting from eye movement. The classification accuracy of the system was 80% when using MLP, 93% when using SVM [14]. Thibhika Ravichandran, Nidal Kamel ,Abdulhakim A. Al-Ezzi ,Khaled Alsaih ,Norashikin Yahya(2021) They classified four separate eye motions using EOG data obtained from four sensors positioned on the eye movement control muscles in both horizontal and vertical directions.…”