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Background <p>Brain-computer interfaces (BCIs) are increasingly used to support rehabilitation and assistive communication. Individual traits such as age, cognitive function, attention, and mental state have been linked to variability in BCI performance. However, these factors have not been comprehensively evaluated across paradigms and populations.</p> Methods <p>A systematic review was conducted following PRISMA 2020 guidelines and registered in the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42024600285). PubMed and Web of Science databases were searched through June 2025 for studies reporting electroencephalography (EEG)-based BCI performance metrics stratified by age, cognition, attention, or psychological state. Twenty-five human studies were included after screening. Risk of bias was assessed using validated appraisal tools.</p> Results <p>Across the 25 included studies, visual paradigms such as&#xa0;P300 event-related potential&#xa0;and&#xa0;steady-state visual evoked potential (SSVEP)&#xa0;showed stable performance across age groups.&#xa0;Motor imagery (MI)-based systems demonstrated higher sensitivity to cognitive and developmental differences. Attention scores and mental rotation were positively associated with EEG signal clarity and classification accuracy. Fatigue, motivation, and training duration influenced user responsiveness.</p> Conclusion <p>Age and cognitive traits impact BCI performance and system adaptability. To optimize usability in diverse populations, future BCI applications should integrate individualized training strategies, real-time feedback mechanisms, and standardized evaluation metrics.</p>

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Influence of age, cognitive function, attention, and mental state on the effectiveness of EEG-based brain-computer interface device use: a systematic review

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月27日

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  • Xinyue Niu,
  • Min Yuan,
  • Dong Wang

摘要

Background

Brain-computer interfaces (BCIs) are increasingly used to support rehabilitation and assistive communication. Individual traits such as age, cognitive function, attention, and mental state have been linked to variability in BCI performance. However, these factors have not been comprehensively evaluated across paradigms and populations.

Methods

A systematic review was conducted following PRISMA 2020 guidelines and registered in the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42024600285). PubMed and Web of Science databases were searched through June 2025 for studies reporting electroencephalography (EEG)-based BCI performance metrics stratified by age, cognition, attention, or psychological state. Twenty-five human studies were included after screening. Risk of bias was assessed using validated appraisal tools.

Results

Across the 25 included studies, visual paradigms such as P300 event-related potential and steady-state visual evoked potential (SSVEP) showed stable performance across age groups. Motor imagery (MI)-based systems demonstrated higher sensitivity to cognitive and developmental differences. Attention scores and mental rotation were positively associated with EEG signal clarity and classification accuracy. Fatigue, motivation, and training duration influenced user responsiveness.

Conclusion

Age and cognitive traits impact BCI performance and system adaptability. To optimize usability in diverse populations, future BCI applications should integrate individualized training strategies, real-time feedback mechanisms, and standardized evaluation metrics.