Bridging Algorithms and Robust Systems through Adaptive Machine Learning in Electrooculography-Based Assistive Technology: A Systematic Literature Review

Deddy Prayama (Department of Electrical Engineering, Faculty of Engineering, Universitas Andalas, Padang, Indonesia Department of Information Technology, Politeknik Negeri Padang, Padang)
Muhammad Ilhamdi Rusydi (Department of Electrical Engineering, Faculty of Engineering, Universitas Andalas, Padang)
Budi Rahmadya (Department of Computer Engineering, Faculty of Information Technology, Universitas Andalas, Padang)
Minoru Sasaki (Department of Mechanical Engineering, Gifu University, Gifu)

Journal of Information Systems Engineering and Business Intelligence
Vol. 12 No. 3 (2026): October

Background: Electrooculography (EOG) has emerged as a promising biosignal modality for assistive technology because it enables eye movement-based human–computer interaction for individuals with motor impairments. However, research on EOG-based assistive systems remains fragmented across signal processing, machine learning, adaptive learning, multimodal integration, and deployment-oriented implementation. Objective: This study aims to systematically review adaptive machine learning approaches in EOG-based assistive technology, identify current research trends and challenges, and develop a conceptual framework for bridging adaptive intelligence and real-world deployment. Methods: A Systematic Literature Review (SLR) was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline. Literature searches were performed in IEEE Xplore, Scopus, PubMed, Springer Nature, Sage, and ScienceDirect. After applying inclusion and exclusion criteria and quality assessment procedures, 60 primary studies published between 2015 and 2025 were selected for qualitative descriptive and thematic synthesis. Results: The findings show a transition from conventional signal processing, threshold-based recognition, and handcrafted feature extraction toward deep learning, transfer learning, domain adaptation, and multimodal adaptive systems. The review also identifies a persistent deployment gap, where high-performing models often require computational resources that may not be suitable for portable, low-cost, and real-time assistive devices. To address this issue, this study proposes the Adaptive EOG Deployment Framework (AEDF), which integrates signal adaptivity, model adaptivity, and deployment adaptivity. Conclusion: Adaptive machine learning is a key enabler of next-generation EOG-based assistive technology. The proposed AEDF provides a unified perspective for developing robust, generalizable, efficient, and user-centered assistive systems that can better support real-world deployment. Keywords: Electrooculography, Adaptive Machine Learning, Assistive Technology, Human Computer Interface, Deployment, Systematic Literature Review