Adding Real-Time Bayesian Ranks to Error-Related Potential Scores Improves Error Detection and Auto-Correction in a P300 Speller
Bibliographic record
Abstract
Brain-computer interface (BCI) spellers could improve access to communication for people with profound physical disabilities; however, improved speed and accuracy of these spellers is required to make them practical for everyday use. Here we introduce the combination of P300-speller confidence with the error-related potential (ErrP) to improve online single-trial error detection and correction accuracies in a BCI speller. First, we present a mechanism for obtaining P300-confidence using a real-time Bayesian dynamic stopping framework that makes novel use of additional stimuli that occur due to epoch and filter delays. Second, we propose an ensemble of decision trees to combine ErrP and P300-confidence features. Third, we describe the unique attentional differences between error and correct feedback in our spelling interface and discuss how these differences affect ErrP physiology. We tested online error detection on 11 typically developed adults using a BCI system trained on a previous day and found an average sensitivity of 86.67% and specificity of 96.59%. Automatic correction increased selection accuracy by 13.67% and utility grew by a factor of 4.48. We found, however, that the improved performance was primarily attributable to the inclusion of P300 confidence in error detection, calling into question the significance of single-trial ErrP detection.
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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 | 0.002 | 0.014 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".