Bibliometric-enhanced Systematic Literature Review of EEG in Education: Learning Concepts, Computational Methods, and Research Opportunities

Application of electroencephalography (EEG) in educational research has grown substantially, yet a comprehensive integration of methodological frameworks, educational constructs, computational methods, and research gaps remains limited. This study applies a Bibliometric-enhanced Systematic Literature Review (BenSLR) to provide a systematic overview of EEG in education. Literature was extracted from Scopus, screened, and analyzed, with keyword co-occurrence evaluated using VOSviewer and emerging trends visualized through an Enhanced Strategic Diagram via BiblioPlot. Key findings include engagement, attention, and learning style as prominent constructs, with machine learning and deep learning frequently employed for modeling complex cognitive states. EEG signal processing, feature extraction, and assessment of cognitive and affective states were recurrent across studies. Innovative interventions such as virtual reality and neurofeedback demonstrate EEG's role in supporting adaptive and individualized learning experiences. Challenges remain in linking neural markers with observable learning behaviors, extending measurements beyond attention and working memory, and enhancing predictive model generalizability. The study demonstrates BenSLR's potential to integrate qualitative and quantitative perspectives and offers a transferable approach for other research areas to develop methodologies and evidence-based educational interventions.