Reproducibility in Brain-Computer Interface Research: A Replication-Based Analysis
Bibliographic record
Abstract
In this paper, we have discussed the reproducibility workflow of published Brain-Computer Interface research articles and remarked on the same by replicating two papers having multiple similarities, starting from the same dataset to the classification stages. We followed a step-by-step approach while replicating the work and documenting the assumptions and interpretations made during the process. Finally, we compared the results and discussed how the documentation in BCI research has evolved over 20 years. Through trial and error implementations and calculated deductions, this paper helps determine the importance and relevance of proper documentation, efficient workflows, and the pressing need for direction-specific information flow in the growing field of Brain Computing Interface applications.
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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 | 0.504 | 0.848 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".