EEG-based Human Recognition Using Steady-State AEPs and Subject-Unique Spatial Filters
Bibliographic record
Abstract
In recent years, brainwaves (EEG) have gained increasing attention in the field of biometric authentication because they feature vital advantages being more secure and impossible to replicate. In this paper, a new approach for the EEG-based biometric recognition system is proposed using steady-state Auditory Evoked Potentials (AEPs). This class of modular brainwaves adds extra features to the system like cancelability and two-step authentication. To investigate the biometric potential of AEPs, brainwaves from 40 subjects were recorded while being stimulated by multiple auditory tones modulated at two frequency bands; 40 Hz (m-40) and 80 Hz (m-80). Each subject participated in two sessions on two different days for time-permanence evaluation. Brain-Computer Interface (BCI) techniques were adopted here for the rapid estimation of the AEPs using canonical correlation analysis. The energy distribution of the AEPs in different frequency bands represented the subject-unique features. For intra-session setup, correct recognition rates up to 96.46% and equal error rates as low as 0% were achieved using the m-80 stimulation over all the 40 subjects. Moreover, results across different sessions showed high recognition rates (94.5 - 96.5%) and low error rates (2 - 4%) over the same number of subjects. These results show that AEPs carry subject discriminating features allowing the possibility of employing AEPs 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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".