Using brain-computer interfaces to determine the location of missing people
Bibliographic record
Abstract
Low-cost, non-invasive electroencephalography (EEG) headsets have become increasingly popular in recent years. Most noteworthy is the adoption of brain-computer interfaces (BCIs) by the non-academic community. Traditionally, these devices were used to assist persons with disabilities, but there has been a large momentum shift to applications for the regular consumer such as cognitive monitoring, cognitive well-being, entertainment, and personal software development through API's and other software development tools. It is believed that the acceptance and interest will continue to grow. We believe that BCIs will be incorporated into everyday life and could be used to benefit society. The application we propose in this paper is determining the location of missing people. Here we describe the background and methodology behind our approach to test the hypothesis that a BCI system can indeed be used to narrow down the location of a missing person, which includes the procedure for ongoing experimental work. BCI systems can be incorporated into games, also known as serious games, and entertainment environments such as movies and YouTube videos. The increased interest in serious games and user-generated content make these the perfect media for our research.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".