Thick Data Analytics through Ensemble Techniques: Identifying Personalized EEG Biometrics based on Eye State Prediction
Bibliographic record
Abstract
Thick data analytics are being pursued to break the barriers of using the big data predictive analytics for small datasets.The main objective of this paper is to improve the performance of the EEG for biometric authentication using eye blinking brain signals through the use of ensembles techniques.Biometric identification differs largly from the other EEG eye movement analytics applications such as detecting epileptic seizure, identification of stress feature or detecting driving drowsiness as it requires high model rubstness and accuracy.A perfect biometric should be unique, universal and permanent over time.Previous analytical approaches on eye movement failed to show the reliability of the the brain signals to distinguish individuals based on the properties of eye-movements seen as time-signals and for this reason the eye movement have not been considered as a possible solution for a biometric system.This paper's primary focus is on the use of ensemble methods to secure the robustness of the person identification from the EEG eye movement waves.Our approach is a multitier one and it start with training notable binary classification models for biometic identification using eye movement.The training tier is followed by ensemble learning (boosting, bagging, and stacking algorithms) to narrow the differences of accuracy gap among classifiers.The classifier's robustness has been measured with the help of variety of accuracy measures including the Matthews correlation coefficient (MCC).The third tier is guage the person prediction model stability using the AUROC (Area Under the Receiver Operating Characteristics) metric.The results obtained in this study proves that it is possible to use an eye tracking based biometric for detection of person identity with reasonably high sensitivity and specificity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".