Limited value of a common spatial patterns approach to online discrimination of left- and right-hand motor imagery in a pediatric sample
Bibliographic record
Abstract
Background Applications of brain-computer interfaces (BCIs) in pediatric rehabilitation are expanding. However, it is unclear whether popular BCI paradigms developed for adults are feasible in children. This study evaluated, in a typically developing pediatric sample, a time-honored, adult, motor imagery BCI paradigm that discriminates between imagined left- and right-hand movements.Methods We developed an electroencephalographic pediatric BCI with visual-auditory feedback through a game interface controlled by left- and right-hand motor imagery (MI). The BCI was evaluated in one offline (with sham feedback) and four online (with real-time classifier feedback) sessions with 11 typically developing children aged 9–14 years. The BCI was personalized to each child, via a well-established adult pipeline, namely, a regularized linear discriminant classifier with selected common spatial patterns in mu and beta bands as inputs.Results Unlike in adults, the online child-specific BCI demonstrated limited discrimination between left and right-hand MI using spatial features (52 ± 9%). Only left-hand MI versus rest in a retrospective analysis with personalized feature sets reached 70 ± 3%.Conclusions Our findings suggest that cortical activity corresponding to MI in our pediatric sample departed from well-documented, conspicuously lateralized adult patterns. Further investigation of developmental MI patterns is warranted to identify a pediatric approach to MI BCI.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".