The BCI Glossary: a first proposal for a community review
Bibliographic record
Abstract
The description of Brain-Computer Interfaces (BCI) can lead to confusion because of the high heterogeneity of devices, protocols, and applications. Besides, different professional categories are involved: end-users, clinicians, therapists, and engineers; each one having different conceptions of BCI-related terms. This can cause misunderstandings and errors, and it makes it impossible to compare different systems and their performances. The IEEE P2731 working group has been working on a standardized glossary for BCI research, together with a functional model for BCI. Here, we are presenting a first version of the BCI glossary, generated by the collective effort of the working group. One hundred fifty-three terms have been identified to be critical for describing in a standardized way BCI systems and their related aspects (e.g., the neurophysiological characteristics of the neural signals recorded). Each term has been provided with a definition, merged from multiple ones proposed by working group members, with appropriate references to the current state of the art. Finally, we are asking for feedback and suggestions about this first version of the BCI glossary to the wider community of BCI users and researchers. External inputs will improve the glossary, which will become, after further revisions, an official IEEE standard.
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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 | 0.023 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.026 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 0.021 |
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".