A New Approach for EEG-Based Biometric Authentication Using Auditory Stimulation
Bibliographic record
Abstract
In this paper, a new approach is followed for the human recognition task using brainwave responses to auditory stimulation. A system based on this class of brainwaves benefits extra features over conventional traits being more secure, harder to spoof, and cancelable. For this purpose, EEG signals were recorded from 21 subjects while listening to modulated auditory tones in a single- and two-session setups. Three different types of features were evaluated based on the energy and the entropy estimation of the EEG sub-band rhythms using narrow band Gaussian filtering and wavelet packet decomposition. These features are classified using discriminant analysis in identification and verification modes of authentication. Based on the achieved results, high recognition rates up to 97.18% and low error rates down to 4.3% were achieved in single session setup. Moreover, in a two-session setup, the proposed system in this paper is shown to be more time-permanent in comparison to previous works.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".