Abstract

The rapid evolution of Human-Computer Interaction (HCI) has led to the development of innovative interfaces that enable seamless communication between humans and machines. Traditional input devices such as keyboards, mice, and touchscreens are not suitable for individuals with severe motor disabilities, particularly those suffering from paralysis, amyotrophic lateral sclerosis (ALS), or locked-in syndrome. These individuals retain cognitive abilities but lack the physical means to communicate effectively, resulting in complete dependency on caregivers. This paper presents a biosignal fusion-based HCI control system that utilizes Electrooculography (EOG) signals for eye-blink detection. The proposed system captures electrical potential generated by eye movements using non-invasive electrodes, processes these signals through embedded hardware, and translates blink patterns into meaningful commands. The system integrates multiple components, including signal acquisition, filtering, feature extraction, pattern recognition, and communication modules, to provide a reliable and efficient interaction mechanism. A key contribution to this work is the implementation of a low-cost, real-time system using Arduino UNO R4 Minima and BioAmp EXG Pill. The system achieves high accuracy in blink detection (96.2%) and low latency (approximately 1.5 seconds), making it suitable for real-time applications. Additionally, a web-based interface with multilingual support and text-to-speech functionality enhances accessibility and usability for diverse users. Experimental analysis demonstrates that the proposed system outperforms traditional assistive technologies in terms of cost, efficiency, and adaptability. The system provides a scalable and practical solution for improving the quality of life of individuals with communication impairments.