Mapping Brain-Computer Interface Research in Depression (1995-2026): A Bibliometric Analysis

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.

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.

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.

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.