Human recognition using transient auditory evoked potentials: a preliminary study
Bibliographic record
Abstract
This study presents a new technique for human recognition using transient auditory evoked potentials (AEPs). AEPs are electrical potentials that are triggered by stimulating ears with auditory stimulus reflecting the neural response from the cochlea to the auditory cortex. These signals feature some advantages over conventional biometric traits as they cannot be easily forged or stolen like fingerprints or faces. Moreover, these signals are cancellable and can be changed by modifying the auditory stimulus. This allows system reuse even if the registered signal was breached. To investigate the biometric potential of this signal, a database of ten subjects was collected where transient AEPs signals were recorded by stimulating the left and the right ears separately. Machine learning techniques were employed to extract unique features for each subject using 1D convolutional neural network. The proposed system was evaluated over single‐session and two‐session recordings. Moreover, a fusion of left and right ear stimulated AEP signals was adopted for performance improvement. Using single‐session and two‐session recordings, the proposed system achieved a correct recognition rate over 95% and an equal error rate below 7%. The achieved results show that AEPs carry subject discriminative features allowing the possibility of employing AEP signal as a biometric trait.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".