EEG markers of cognitive load and mental fatigue in university students: a systematic review

Introduction:

University students are frequently exposed to high cognitive demands, which can lead to sustained cognitive load and mental fatigue and may negatively affect learning outcomes and well-being. Electroencephalography (EEG) provides a non-invasive, real-time window into neural activity associated with cognitive effort. This systematic review aims to synthesise current evidence on EEG markers of cognitive load and mental fatigue in university students and other young adult learners.

Methods:

This systematic review was conducted in accordance with the PRISMA 2020 statement (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). We systematically searched electronic databases (e.g., PubMed, Scopus and Web of Science) for peer-reviewed studies on EEG-based assessment of cognitive load/mental fatigue in healthy young adults (typically university students). Records were screened against predefined inclusion/exclusion criteria, and eligible studies were included for data extraction.

Results:

Seven studies met the inclusion criteria, comprising 179 participants (sample sizes 7–43). Paradigms varied across examination stress, language-mediated learning, multimedia manipulations, and classroom tasks, limiting direct comparisons. Cognitive load was most often reflected in changes in theta, alpha, and beta power and band ratios. One study found that self-reported difficulty correlated positively with beta activity at T3 (r = 0.309, p < 0.05) and negatively with learning performance (r = −0.391, p < 0.01). Studies manipulating multimedia design principles generally reported lower load indicators and better test performance when principles were applied.

Conclusions:

EEG-based markers, particularly theta and posterior alpha activity, show promising potential for indexing cognitive load and mental fatigue in university students. However, methodological variability and small sample sizes hinder the development of robust, generalisable EEG metrics. Future research should prioritise standardised protocols, larger cohorts and transparent reporting to support the use of EEG for monitoring cognitive strain in educational and applied settings.