An Unsupervised Clustering and Markov Chain-Based Approach for Assessing Performance During Online User Training for Mental Imagery EEG-BCIs
Bibliographic record
Abstract
Brain-computer interfaces (BCIs) have many potential applications for individuals with physical disabilities; however, their usage is limited due to unreliable performance. While users can improve performance via training, the effectiveness of current training approaches may be limited by inaccurate performance assessments and confusing feedback. Herein, we render recently proposed user performance assessments for mental imagery electroencephalography (EEG)-BCIs conducive to online deployment. The approach uses$K$-means clustering to segment the EEG signal space into pattern states and then models transitions between these states using Markov chains. A metric, taskDistinct , uses the Markov chain steady-state distributions to measure user ability to produce task-specific EEG patterns. The objective of this work was twofold: first, to assess the sensitivity of the adjusted metrics to performance variations throughout a session; and second, to examine the influence of the number of pattern states in the models on this sensitivity. To meet these objectives, we performed pseudo-online analyses where the taskDistinct metric was computed with various numbers of pattern states throughout simulated data collection sessions. Analysis revealed significant positive correlations between the adapted taskDistinct metric and other performance metrics. Additionally, the metric sensitivity to performance changes was not significantly affected by the number of pattern states. The results indicate that the adapted Markov chain-based metrics could be used for assessing performance in online user training for mental imagery EEG-BCIs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".