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A Bibliometric Analysis of EEG-Based Brain Training and Robotic Feedback Integration in Motor Control Research

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Maria Caroline Wojtila(1Mail), Gerard Anthonius Juswanto(2), Anne Gracia(3), Maria Caroline Wojtila(4), Tim Valentino Lakusa(5), Rivo Panji Yudha(6), Sandra Yap Donggorables(7), RM Pangeran Saleh(8), Retnaningsih R(9), Ujan Taufik Hidayah(10), Jumraini Tammase(11), Dodik Tugasworo(12), Syahrul S(13),
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Available online: 2025-08-02  |  Published : 2025-08-02
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Abstract


Background and Objective: The integration of EEG-based brain training with robotic feedback systems represents an emerging paradigm in motor control research and neurorehabilitation. This study provides the first comprehensive bibliometric analysis to map the intellectual structure, research trends, and collaboration patterns in this rapidly evolving interdisciplinary field.Methods: A systematic bibliometric analysis was conducted using Scopus and Web of Science databases, identifying 197 relevant publications from 2015 to 2025. Data extraction included complete bibliographic records, citation metrics, and keyword assignments. Analysis was performed using VOSviewer for network visualization, Biblioshiny for comprehensive bibliometric computations, and statistical analysis for temporal trends, geographic distribution, author productivity, and thematic clustering.Results: The field demonstrated exponential growth with peak output in 2022 (40 publications, 185.7% increase). Publications achieved exceptional citation impact (average 49.0 citations per paper) with 19 papers receiving >100 citations. Five distinct research clusters were identified: EEG-based brain training, robotic rehabilitation systems, clinical stroke applications, brain-computer interface technology, and motor control learning. Singapore emerged as the leading research hub (16.2% of publications) despite small geographic size, while international collaboration rates (69.2%) significantly exceeded typical biomedical research patterns. Stroke rehabilitation dominated clinical applications, with open-access venues (particularly Frontiers journals) representing primary publication channels.Conclusions: EEG-robotic integration research has successfully transitioned from an emerging area to an established interdisciplinary field with sustained research momentum and global collaborative networks. The findings provide strategic guidance for research institutions, funding agencies, and policymakers, recommending prioritization of international partnerships, coordinated infrastructure development, and focused clinical implementation in stroke rehabilitation while expanding to other neurological conditions.

Keywords


EEG; brain training; robotic feedback; motor control; bibliometric analysis; brain-computer interface; neurorehabilitation; neurofeedback

References


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