SynthesisJournal of neuroengineering and rehabilitation2025
Influence of age, cognitive function, attention, and mental state on the effectiveness of EEG-based brain-computer interface device use: a systematic review.
Synthesis in Journal of neuroengineering and rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Dataset Fragmentation, Cognitive Variability, and Reproducibility Challenges in EEG-Based Brain-Computer Interfaces: A PRISMA-Based Systematic Review.Sensors (Basel, Switzerland) · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundBrain-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.
methodsA 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.
resultsAcross 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.
conclusionAge 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.