Global trends in EEG‑based BCI for exoskeletons, prostheses, and rehabilitation robots: A bibliometric and topic modeling synthesis

Electroencephalography (EEG)-based brain computer interface (BCI) has become a critical technology for intelligent exoskeletons, prosthetic systems, and rehabilitation robots, allowing direct neural control and adaptive human-robot interaction. This study offers a comprehensive synthesis of this rapidly evolving domain, employing an innovative, multidimensional methodological framework that integrates bibliometric analysis with machine learning-driven topic modeling. The combined use of these approaches enables a distinctive examination of both publication trends and latent thematic structures within literature. A total of 2064 articles retrieved from the Scopus database were analyzed for bibliometric indicators and thematic structures. The results showed that IEEE Transactions on Neural Systems and Rehabilitation Engineering, Clinical Neurophysiology, and the Journal of Neural Engineering were the most significant journals in the discipline. Furthermore, China, the United States, India, and Germany were recognized as the largest contributors. Additionally, eight latent research areas were identified, showing notable progress in EEG decoding and classification for BCI applications. These advancements have enabled diverse BCI control paradigms, including sensorimotor rhythm modulation and asynchronous BCI systems, to enhance user interaction. Furthermore, emerging research directions such as continuous movement decoding and kinematics reconstruction are attracting growing scholarly attention. These findings present theoretical, practical, and methodological contributions, providing guidelines for future study and practical implications for clinical and industrial stakeholders.