Case report: Novel use of clinical brain-computer interfaces in recreation programming for an autistic adolescent with co-occurring attention deficit hyperactivity disorder
Bibliographic record
Abstract
Background: In recent years, several autistic children and youth have shown interest in Holland Bloorview Kids Rehabilitation Hospital's clinical brain computer interface (BCI) program. Existing literature about BCI use among autistic individuals has focused solely on cognitive skill development and remediation of challenging behaviors. To date, the benefits of recreational BCI programming with autistic children and youth have not been documented. Purpose: This case report summarizes the experiences of an autistic male adolescent with co-occurring attention deficit hyperactivity disorder using a BCI for recreation and considers possible benefits with this novel user population. Methods: A single retrospective chart review was completed with parental guardian's consent. Findings: The participant demonstrated enjoyment in BCI sessions and requested continued opportunities to engage in BCI programming. This enjoyment correlated with improved Canadian Occupational Performance Measure (COPM) scores in BCI programming, outperforming scores from other recreational programs. Additionally, clinicians observed changes in social communication efforts and self-advocacy in this first autistic participant. Conclusion: The use of brain computer interfaces in recreational programming provides a novel opportunity for engagement for autistic children and youth that may also support skill development.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".