Mapping Brain-Computer Interface Research in Depression (1995-2026): A Bibliometric Analysis.
- 1Boston University's College of Engineering, Boston, Massachusetts, USA.
- 2Department of Thoracic Surgery, Anhui Provincial Children's Hospital, Hefei, China.
Abstract
Introduction
Brain-computer interfaces (BCIs) have emerged as promising tools for neuromodulation and emotion regulation in depression. This study aimed to systematically map the global research landscape, thematic evolution, and emerging trends of BCI applications in depressive disorders through bibliometric analysis.Methods
Data were retrieved from the Web of Science Core Collection and analyzed using the R package bibliometrix, VOSviewer, and CiteSpace. Publication trends, country and institutional contributions, journal impact, author influence, and keyword-based clustering and burst analysis were comprehensively evaluated.Results
A total of 239 publications authored by 1284 researchers from 239 institutions across 39 countries were identified. The United States and China had the highest publication outputs, while the United Kingdom showed high citation impact. Laureate Institute for Brain Research was the most productive institution and showed the strongest institutional collaboration, while NeuroImage: Clinical showed the highest journal h-index. Masaya Misaki was the leading author according to publication and citation indicators. Keyword analysis identified six major thematic clusters involving neurophysiological and cognitive features, EEG asymmetry and affective regulation, clinical neurofeedback, connectivity and self-regulation, fMRI-based emotion regulation, and psychiatric symptom/network correlates. "Emotion" showed the strongest keyword burst, while recent bursts in "real-time fMRI neurofeedback," "prefrontal cortex," and "emotion regulation" indicated increasing research attention to circuit-level regulation.Conclusion
The field has evolved from EEG-based characterization toward network-level and real-time neurofeedback research. These findings identify emerging research directions while highlighting the need for further methodological standardization and clinical validation.
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BibTeX
@article{jiang2026mapping,
title = {{Mapping Brain-Computer Interface Research in Depression (1995-2026): A Bibliometric Analysis.}},
author = {Yicheng Jiang and Xianlun Duan},
journal = {Brain and behavior},
year = {2026},
month = nov,
doi = {10.1002/brb3.71896},
url = {https://europepmc.org/article/MED/42975207},
note = {CC BY},
}
RIS
TY - JOUR
TI - Mapping Brain-Computer Interface Research in Depression (1995-2026): A Bibliometric Analysis.
AU - Yicheng Jiang
AU - Xianlun Duan
PY - 2026
DA - 2026/11/01
JO - Brain and behavior
DO - 10.1002/brb3.71896
UR - https://europepmc.org/article/MED/42975207
AB - Introduction Brain-computer interfaces (BCIs) have emerged as promising tools for neuromodulation and emotion regulation in depression. This study aimed to systematically map the global research landscape, thematic evolution, and emerging trends of BCI applications in depressive disorders through bibliometric analysis.Methods Data were retrieved from the Web of Science Core Collection and analyzed using the R package bibliometrix, VOSviewer, and CiteSpace. Publication trends, country and institutional contributions, journal impact, author influence, and keyword-based clustering and burst analysis were comprehensively evaluated.Results A total of 239 publications authored by 1284 researchers from 239 institutions across 39 countries were identified. The United States and China had the highest publication outputs, while the United Kingdom showed high citation impact. Laureate Institute for Brain Research was the most productive institution and showed the strongest institutional collaboration, while NeuroImage: Clinical showed the highest journal h-index. Masaya Misaki was the leading author according to publication and citation indicators. Keyword analysis identified six major thematic clusters involving neurophysiological and cognitive features, EEG asymmetry and affective regulation, clinical neurofeedback, connectivity and self-regulation, fMRI-based emotion regulation, and psychiatric symptom/network correlates. "Emotion" showed the strongest keyword burst, while recent bursts in "real-time fMRI neurofeedback," "prefrontal cortex," and "emotion regulation" indicated increasing research attention to circuit-level regulation.Conclusion The field has evolved from EEG-based characterization toward network-level and real-time neurofeedback research. These findings identify emerging research directions while highlighting the need for further methodological standardization and clinical validation.
SN - 2162-3279
ER -