Improving bit rate in an auditory BCI: Exploiting error-related potentials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The error-related potential (ErrP) can inform the correction of brain-computer interface (BCI) mistakes, but it has thus far been incorporated only into visual BCIs, with mixed success. Given that ErrPs are thought to have higher impact when BCI accuracy is relatively low, we sought to identify the aurally evoked ErrP and investigate its auto-corrective value in auditory BCIs, which typically yield lower accuracies than visual BCIs. We implemented an auditory P300 BCI with four selectable items. Each of nine typically developed participants attempted to spell letter sequences on two separate days. Erroneous feedback was detected by (i) making use of the ErrP, (ii) assessing BCI selection confidence, and (iii) combining these two pieces of information into a hybrid detector. ErrPs were detected with an average cross-validation area under the curve of 0.946. Simulated automatic correction by reverting to the second-ranked letter improved participant-wise information transfer rate by 2.3 bits/minute when errors were detected by the hybrid method. The results suggest ErrP-based error correction can be used to make a substantial improvement in the performance of auditory BCIs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it