A brain-computer interface based on mental tasks with a zero false activation rate
Bibliographic record
Abstract
Most brain-computer interface applications in real-life suffer from the high rate of false activations. The ultimate goal when designing brain-computer interfaces is to reach the zero false activation rate while the true activation rate is kept at a high level. In this study, a brain-computer interface design is shown to have a zero false activation rate. The interface is based on different mental tasks. It is custom designed to every subject and to every mental task. The most discriminatory mental task for each subject is determined. We use the autoregressive modeling as the feature extraction method. The classification is performed by a radial basis function neural network. The EEG signals of four subjects during five mental tasks are used. The order of autoregressive model is varied from 2 to 20 and custom designed for each mental task and each subject in the cross-validation stage. The performance of the brain-computer interfaces based on the most discriminatory mental tasks is shown to be highly promising since the false positive rate reaches zero while the mean of the true positive rate obtained is above 70%.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".