EOG Signal Analysis for Efficient Human-Computer Interaction

Abstract

Electrooculography (EOG) is an essential technique in bio-signal processing, capturing eye movement and position through electric potentials around the eyes. This technology has gained significant attention for its application in Human-Computer Interaction (HCI), particularly for individuals with disabilities. This paper explores the methodologies of EOG signal analysis, its applications in HCI, and strategies for optimizing signal processing to enhance interaction efficiency. We review various EOG signal acquisition methods, pre-processing techniques, feature extraction, and classification algorithms. Furthermore, we discuss the challenges and future directions for EOG-based HCI systems, aiming to improve accessibility and user experience.

Brain-Computer Interfaces (BCIs) based on EOG have significantly impacted daily life, gaming, physical medicine, and aviation. These systems capture user intentions, perceptions, and motor choices by translating physiological signals into commands for external devices, enabling the execution of user-intended functions. EOG signals can recognize and classify eye movements through active or passive engagement, facilitating the control of output devices. Research in the aviation industry explores EOG-BCIs as alternatives to manual commands and as tools for streamlining user intentions.

This study reviews the last two decades of EOG-based BCI experiments, presenting current systems and inspiring future developments. We first discuss fundamental aspects of EOG-BCI research, including signal capture, device specificity, feature extraction, translation algorithms, and interface instructions. Additionally, we summarize EOG-based BCI applications in both real and virtual environments, beyond aviation. We conclude by addressing the current limitations of EOG devices and offering recommendations for future design investigations.