Exploiting error-related potentials in cognitive task based BCI
Bibliographic record
Abstract
Abstract Brain-computer interfaces (BCIs) can make mistakes in recognizing user intention. It has been well-established that for reactive and motor imagery (MI) BCIs, an error-related potential (ErrP) occurs subsequent to such mistakes, and can be used to improve BCI performance. However, the presence of ErrPs in active BCIs based on non-MI cognitive tasks has not been confirmed. In this study, we attempted to elicit ErrPs in a BCI based on non-MI mental tasks. Twelve typically developed young adults participated in two sessions each. Participants performed multiple iterations of five different mental tasks (mental arithmetic, counting, word generation, figure rotation and idle state) to ‘knock down’ one of 4 targets on a graphical interface (each mental task was associated with a different target). To simulate errors, a random subset of 20% of the trials were followed by incorrect feedback (i.e., the wrong target fell). Our findings confirmed the presence of an interaction ErrP, with a negative peak at ∼180 ms, followed by two positive peaks, respectively, at ∼400 and ∼630 ms post-feedback onset. The classification of mental tasks and error versus non-error trials were both performed using a pseudo-online paradigm where the last quarter of trials were used for testing. For binary (task and idle) and ternary (2 tasks and idle) classification, across-participant average accuracies of 76% ± 12 and 63%±12, respectively, were attained. An average area under curve (AUC) of 0.83 was reached across participants for the detection of ErrPs. After applying ErrP-based error correction, the average binary and ternary classification accuracies of mental tasks improved by 9% and 14%, respectively. Our findings support the addition of ErrP detection and ErrP-informed correction to maximize the accuracy of BCIs based on cognitive tasks.
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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 | 0.006 |
| 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) | 0.001 | 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".