Riemannian geometry metric-based visual feedback for BCI user training: towards exploratory learning of motor imagery
Bibliographic record
Abstract
Objective Despite their many emerging applications, practical use of brain–computer interfaces (BCIs) is often impeded by BCI-inefficiency, that is, the failure of the technology to decode neural modulations with sufficient accuracy. Recent evidence suggests that ineffective user training, namely feeding back to the user performance metrics that do not relate to the future performance of the BCI, may be obstructing users from learning how to produce machine-discernible sensorimotor rhythm modulations. Here, we use models of human skill acquisition to design a user-training interface to address these challenges.Approach We presented feedback via Riemannian geometry-based user performance metrics, which were validated via BCI simulation as bearing relation to future classifier performance. We subsequently evaluated the effect of the proposed feedback on users’ interpretation of their performance.Results Regression models showed that the metrics accounted for 53%–62% of intersubject variation in future classification accuracy with common BCI classifiers, thereby substantiating the use of the metric to guide user training. Participants were significantly better (p < 0.05) at detecting user performance changes with Riemannian metric-based feedback than with classifier feedback.Conclusion Our findings suggest that the proposed metrics can be effective for both assessing and communicating user performance, and therefore, warrant further investigation.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".