Using EEG to Predict and Analyze Password Memorability
Bibliographic record
Abstract
Brain-Computer Interfaces (BCIs) have given us insight into the human brain, and as sensors grow cheaper and smaller, and devices get more connected, more researchers from new domains are motivated to use BCIs. In this 19-participant lab study, we use off-the-shelf BCIs to investigate password memorability and recall. We record electroencephalogram (EEG) potentials collected by BCIs upon presenting passwords of different characteristics to participants while asking them to memorize these passwords, and then recall them. Features from the EEG signals are extracted in three domains: power spectrum from the frequency domain, statistics from the time domain, and wavelet coefficients from the time-frequency domain. Lasso feature selection method is used, and the selected parameters and feature subsets are submitted for classification with two classes, recalled and not recalled, based on the user's subsequent recall of the passwords. Results show discriminating features of EEG signals in the two different classes, achieving a classification accuracy of 88%. Our results indicate that it may be possible to predict subsequent password recall based on EEG activity during password presentation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".