The BCI Glossary: a first proposal for a community review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it