Bibliometric Analysis of EEG and Eye Tracking Techniques in Executive Function Research

I. S. Mannesa, Muh. Nur Haq; Nur Fathoni, Aldinda Najib; Kusrohamaniah, Sri · 2025 · Crossref

DOI: 10.55549/ijasse.38

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This bibliometric analysis examines the integration of electroencephalography (EEG) and eye-tracking (ET) technologies in executive function (EF) research. The study addresses the need to map the evolution, collaboration patterns, and thematic developments within this interdisciplinary field. Executive functions, encompassing "cold" cognitive processes like working memory and "hot" emotional regulation, are critical for adaptive behavior and academic success. While EEG provides high-temporal-resolution data on neural activity and ET captures visual attention and decision-making behaviors, their combined use offers a holistic view of cognitive mechanisms. The authors note that previous literature lacks a comprehensive bibliometric overview of this specific technological integration, particularly regarding its application in neuropsychology, education, and marketing. The researchers conducted a systematic literature search across Scopus, Web of Science, and PubMed databases, covering publications from 1992 to 2024. Using keywords such as "electroencephalography," "eye tracking," and "executive function," they initially identified 309 documents. After removing duplicates, 212 relevant articles remained for analysis. The study employed R Studio software with the Bibliometrics and Biblioshiny packages to analyze publication trends, author productivity, institutional contributions, and collaboration networks. The methodology followed a two-stage process: document collection and data visualization/interpretation, aiming to identify influential authors, productive institutions, and emerging research topics. Key findings reveal distinct patterns in author productivity and impact. Authors such as Anja, A., Davis, F., and Dimoka, A. achieved the highest citation counts (235 each) with single publications, indicating high individual impact, whereas authors like Baglio, F., and Riva, G. demonstrated higher productivity with four publications each but moderate citation totals. Collaboration networks highlight significant clusters, including groups led by Baglio and Rossetto, who focus on neurocognitive research, and Cabral and Carneiro, who develop empirical approaches for visual cognition. Institutionally, Zhejiang University was the most productive with eight articles, followed by the University of Pennsylvania with seven. Other notable contributors included Poznan University of Economics and Business and the University of Liverpool. The analysis underscores the importance of cross-disciplinary and international collaborations in advancing the field. The study concludes that integrating EEG and ET technologies revolutionizes the investigation of complex cognitive processes by bridging neural and behavioral data. This multidimensional approach surpasses traditional self-report methods, offering deeper insights into EF mechanisms relevant to clinical diagnostics, educational interventions, and marketing strategies. The bibliometric mapping identifies gaps in current literature and highlights the potential for machine learning applications in analyzing complex EEG and ET data. By elucidating global collaboration patterns and thematic trends, the research provides a strategic foundation for future interdisciplinary studies, emphasizing the role of these technologies in enhancing understanding of cognitive development and mental health interventions.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• neuro workload indices