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This systematic review examines the burgeoning field of investigating mathematical efficiency through electroencephalography (EEG), aiming to elucidate the neural substrates and temporal dynamics underlying efficient mathematical processing. Through comprehensive database searches and rigorous inclusion criteria, a total of 15 EEG studies were identified and synthesized. Findings reveal distinct neural oscillations and event-related potentials (ERPs) associated with various facets of mathematical cognition, including numerical magnitude processing, arithmetic operations, working memory engagement, and problem-solving strategies. Furthermore, the review highlights the impact of individual differences, developmental trajectories, and mathematical expertise on EEG-derived measures of mathematical efficiency. Methodological considerations, encompassing experimental design, data preprocessing, and analytical techniques, are critically evaluated to enhance methodological rigor and reproducibility within the field. By consolidating evidence from diverse studies, this systematic review advances our understanding of the neural mechanisms underpinning mathematical cognition and delineates avenues for future research aimed at optimizing mathematical learning and performance through EEG-based approaches.

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A Systematic Review of Electroencephalography (EEG) Studies Investigating Mathematical Efficiency: Current Insights and Future Directions

  • 会议论文

  • 发表日期：

    2026年

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  • Athina Papadopoulou,
  • Panayiotis Vlamos,
  • Aristidis Vrahatis

摘要

This systematic review examines the burgeoning field of investigating mathematical efficiency through electroencephalography (EEG), aiming to elucidate the neural substrates and temporal dynamics underlying efficient mathematical processing. Through comprehensive database searches and rigorous inclusion criteria, a total of 15 EEG studies were identified and synthesized. Findings reveal distinct neural oscillations and event-related potentials (ERPs) associated with various facets of mathematical cognition, including numerical magnitude processing, arithmetic operations, working memory engagement, and problem-solving strategies. Furthermore, the review highlights the impact of individual differences, developmental trajectories, and mathematical expertise on EEG-derived measures of mathematical efficiency. Methodological considerations, encompassing experimental design, data preprocessing, and analytical techniques, are critically evaluated to enhance methodological rigor and reproducibility within the field. By consolidating evidence from diverse studies, this systematic review advances our understanding of the neural mechanisms underpinning mathematical cognition and delineates avenues for future research aimed at optimizing mathematical learning and performance through EEG-based approaches.