Cognitive requirements for effective use of brain-computer interfaces (BCIs) in pediatric populations: A scoping review
Bibliographic record
Abstract
Background Brain-computer interfaces (BCIs) are technologies that may allow children to operate assistive devices by translating their brain signals into commands for the devices. BCI technology requires specific user skills for successful operation. However, the cognitive demands remain relatively unexplored, particularly in pediatric populations, where developmental differences and cognitive variability significantly influence usability and performance. Objective This review explores what has been reported on the cognitive requirements for using BCIs, with a focus on the pediatric population. Methods A systematic search was conducted across six databases (Scopus, Web of Science, Embase, MEDLINE, PsycINFO, and CINAHL) for original research studies involving children aged 5–18 using BCIs for control purposes. Inclusion criteria focused on studies reporting cognitive factors relevant to BCI performance. Data extraction and analysis followed the PRISMA-ScR guidelines. Results Seven studies met the inclusion criteria, highlighting attention, motivation, and processing speed as key factors influencing BCI performance. However, the limited pediatric research suggests significant gaps in understanding the cognitive factors involved in BCI performance. Conclusions Further research is necessary to tailor BCI systems implementation to the unique cognitive and developmental needs of children. Addressing these gaps will enhance BCI usability and effectiveness, promoting greater independence for children with motor disabilities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".