Creating an engaging brain computer interface, electrical stimulation therapy for children with hemiparesis: a pilot study
Bibliographic record
Abstract
BACKGROUND: Perinatal stroke can lead to lifelong physical disabilities, where even small improvements in motor function can increase quality of life. Rapid brain development in children provides an opportunity to harness brain plasticity. Current therapies are minimally effective due in part to the boring, unengaging procedures required to achieve adequate repetitions required for therapeutic benefit. The combination of brain computer interface and functional electrical stimulation (BCI-FES) may be effective for adults with stroke-induced hemiparesis and appears feasible in children. We designed a novel BCI-FES system that uses social media to engage youth. METHODS: The project was informed through engagement with youth patient partners with lived experience. Participants were fitted with a 16channel EEG gel headset. BCI training consisted of 20 trials of attempted target movement. Successful classification was paired with FES of the target movement and allowed the participant to swipe to watch the next video as desired. Youth with perinatal stroke and hemiparesis were then recruited to trial the system. Outcomes included training accuracy, BCI performance (Cohen's kappa), box and blocks, and qualitative interviews to characterize usability and patient experience. RESULTS: Twelve participants (aged 10–23 years) completed three sessions. No adverse events occurred; fatigue was minimal and varied across sessions. System performance varied but most sessions had moderate or better agreement. Average repetitions for all sessions were 167 reps/hour [SD = 55.2 range = 65–283 reps/hour] with FES and 247 reps/hour [SD = 74.9 range = 105–379 reps/hour] with and without FES (attempts and training). Motor outcomes were variable but improved for some. Qualitative feedback suggested higher motivation and enjoyment compared with traditional therapies but also identified frustrations surrounding technical challenges and equipment comfort. CONCLUSION: Informed by users, simple EEG-based BCI can be integrated with FES and social media to enhance upper extremity rehabilitation in youth with hemiparesis. This pilot trial will inform the design of future clinical trials to evaluate efficacy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".