Have We Met Before? Using Consumer-Grade Brain-Computer Interfaces to Detect Unaware Facial Recognition
Why this work is in the frame
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Bibliographic record
Abstract
Much research has been done on the brain’s reaction to seeing faces, but while much of the work has investigated the brain’s conscious reaction to faces, far less work has been done exploring the brain’s unaware reactions using consumer-grade devices. Built on previous work, we describe an experiment conducted using EEGs and consumer-grade Brain-Computer Interface (BCI) headsets to measure the brain’s unaware reaction to seeing faces of three pre-defined recognition classes: no recognition, unaware recognition, and aware recognition. We pre-select images to be shown in each class and display the images in a two-day experiment where participants implicitly learn images tagged as “unaware recognition” for use in the second day. It was found that, outperforming previous works, unaware facial recognitions could be detected with fairly high accuracies using a method that combines multiple sensors from a BCI device and utilizing out-of-the-box classification methods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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