Classifying Faces: Can an Off-The-Shelf System be Effective?
Bibliographic record
Abstract
Objective: Our overall research goal is to use electroencephalography (EEG) to identify recognition of faces by patients with dementia to improve reminiscence therapy. The objective of this paper was to evaluate the performance of the consumer grade Emotiv Insight using a facial recognition event related potential (ERP) task. Good classification accuracy would enable the use of commercial systems such as the Insight to be used for reminiscence therapy. Methods: EEG was recorded with the Insight while obscure and famous facial stimuli were presented; participants confirmed recognition with a button press and oral confirmation. ERPs were averaged across five presentations of each facial stimuli to improve the signal to noise ratio. Classifiers were trained on one session, tested on another session, and the accuracy of each system was compared. Results: The Insight did produce ERPs typical of recognition memory however, it did not perform much better than chance. Event integration and artefacts were a significant problem. Conclusion: The Insight has practical advantages; however, the recording quality is not high enough to achieve adequate classification accuracy.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.013 |
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".