The lived experiences of play and the perspectives of disabled children and their parents surrounding brain-computer interfaces
Bibliographic record
Abstract
Brain-computer interfaces (BCI) offer promise to the play of children with significant physical impairments, as BCI technology can enable disabled children to control computer devices, toys, and robots using only their brain signals. However, there is little research on the unique needs of disabled children when it comes to BCI-enabled play. Thus, this paper explored the lived experiences of play for children with significant physical impairments and examined how BCI could potentially be implemented into disabled children's play experiences by applying a social model of childhood disability. Descriptive qualitative methodology was employed by conducting four semi-structured interviews with two children with significant physical impairments and their parents. We found that disabled children's play can be interpreted as passive or active depending on one's definition and perceptions surrounding play. Moreover, disabled children continue to face physical, economic, and technological barriers in their play, as well as play restrictions from physical impairments. We urge that future research should strive to directly hear from disabled children themselves, as their perspectives may differ from their parents' views. Also, future BCI development should strive to incorporate video games, recreational and entertainment applications/platforms, toys and switch-adapted toys, and power wheelchairs to better support the play of children with significant physical impairments.Implications for RehabilitationAssistive technology research should strive to examine the social, infrastructural, and environmental barriers that continue to disable and restrict participation for disabled children and their families through applying a social model of childhood disability and other holistic frameworks that look beyond individual factorsFuture research that examines the needs and lives of disabled children should strive to directly seek the opinions and perspectives of disabled children themselvesBrain-computer interface development should strive to incorporate video games, recreational and entertainment applications/platforms, toys and switch-adapted toys, and power wheelchairs to better support the play of children with significant physical impairments.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".