Can Children With Perinatal Stroke Use a Simple Brain Computer Interface?
Bibliographic record
Abstract
Background and Purpose: Perinatal stroke is the leading cause of hemiparetic cerebral palsy resulting in lifelong disability for millions of people worldwide. Options for motor rehabilitation are limited, especially for the most severely affected children. Brain computer interfaces (BCIs) sample brain activity to allow users to control external devices. Functional electrical stimulation enhances motor recovery after stroke, and BCI-activated functional electrical stimulation was recently shown to improve upper extremity function in adult stroke. We aimed to determine the ability of children with perinatal stroke to operate a simple BCI. Methods: Twenty-one children with magnetic resonance imaging–confirmed perinatal stroke (57% male, mean [SD] 13.5 [2.6] years, range 9–18) were compared with 24 typically developing controls (71% male, mean age [SD] 13.7 [3.7] years, range 6–18). Participants trained on a simple EEG-based BCI over 2 sessions (10 trials each) utilizing 2 different mental imagery strategies: (1) motor imagery (imagine opening and closing of hands) and (2) goal oriented (imagine effector object moving toward target) to complete 2 tasks: (1) drive a remote controlled car to a target and (2) move a computer cursor to a target. Primary outcome was Cohen Kappa with a score >0.40 suggesting BCI competence. Results: BCI performance was comparable between stroke and control participants. Mean scores were 0.39 (0.18) for stroke versus 0.42 (0.18) for controls (t[42]=0.478, P=0.94). No difference in performance between venous (M=0.45, SD=0.29) and arterial (M=0.34, SD=0.22) stroke (t[82]=1.89, P=0.090) was observed. No effect of task or strategy was observed in the stroke participants. Over 90% of stroke participants demonstrated competency on at least one of the 4 task-strategy combinations. Conclusions: Children with perinatal stroke can achieve proficiency in basic tasks using simple BCI systems. Future directions include exploration of BCI-functional electrical stimulation systems for rehabilitation for children with hemiparesis and other forms of cerebral palsy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".