Architectural development of an electrooculographic interface for biomedical assistive systems: modular signal processing approach

Abstract

The present study introduces a modular electrooculographic (EOG) system designed for real-time acquisition and processing of ocular bioelectrical activity, specifically intended for implementation in assistive biomedical technologies. The system is composed of a sequence of interdependent functional modules, beginning with the collection of surface bioelectrical signals through electrodes positioned in direct contact with the skin surrounding the eyes. This is followed by a signal amplification stage, designed to elevate low-amplitude physiological signals to levels compatible with downstream processing, while maintaining the integrity of the original waveform. The analog signal is then routed through a filtration module that selectively removes external electromagnetic interference and internal biological artifacts, facilitating high-resolution signal clarity. Upon completion of the analog conditioning phase, the signal undergoes analog-to-digital conversion and is transmitted to an external terminal for interpretation and functional application in assistive device control. The design objective of this system emphasizes the preservation of signal fidelity throughout all stages of transformation to support the development of responsive control interfaces for individuals affected by motor dysfunction. The modular design facilitates system scalability and supports the integration of algorithmic processing layers, including adaptive machine learning methods for real-time classification and individual calibration based on physiological variance. Experimental trials verified the system’s capacity to maintain the structural and temporal characteristics of diagnostically relevant EOG signals under dynamic conditions, reinforcing its utility for biomedical signal integration in personalized rehabilitation frameworks and broader human-machine interaction paradigms.