Overt Mental Stimuli of Brain Signal for Person Identification
Bibliographic record
Abstract
Cybersecurity is an important and challenging issue faced by governments, financial institutions and ordinary citizens alike. Secure identification is needed for accessing confidential government information, online bank transaction, person's social network (Facebook, Twitter, Linkedin). Brain signal electroencephalogram (EEG) can play a vital role in ensuring security as it is non-vulnerable and hard to steal. In this article, we develop an EEG based biometric security system. The purpose of this work is to find the best band or the best bands combination of overt mental stimuli of brain EEG signal to identify a person. The Discrete Wavelet Transform (DWT) is used to extract different significant features which separate Alpha, Beta and Theta band of frequencies of the EEG signal. Extracted EEG features of different bands and their combinations such as alpha-beta, alpha-theta, theta-beta, alpha-beta-theta are classified using an artificial neural network (ANN) trained with the back propagation (BP) algorithm. The classification rate shows that Alpha band (84.4%) has higher mapping precision and better convergence rate than the other bands, beta (80%), theta (78.1%) and bands combination as alpha-beta (64.1%), alpha-theta (65.6%), beta-theta (58.8%), alpha-beta-theta (56.9%). Another classifier K nearest neighbor (KNN) is used to verify this result. The classification result of this KNN classifier also shows that alpha band (50%) has higher convergence rate than other bands, beta (40%) and theta (40%). The results of this study are expected to be helpful for future research of overt mental stimuli brain signal based biometric approaches.
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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 | 0.000 |
| 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".