A New Method for EEG-Based Concealed Information Test
Why this work is in the frame
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Bibliographic record
Abstract
Forensic electroencephalogram (EEG)-based lie detection has recently begun using the concealed information test (CIT) as a potentially more robust alternative to the classical comparative questions test. The main problem with using CIT is that it requires an objective and fast decision algorithm under the constraint of limited available information. In this study, we developed a simple and feasible hierarchical knowledge base construction and test method for efficient concealed information detection based on objective EEG measures. We describe how a hierarchical feature space was formed and which level of the feature space was sufficient to accurately predict concealed information from the raw EEG signal in a short time. A total of 11 subjects went through an autobiographical paradigm test. A high accuracy of 95.23% in recognizing concealed information with a single EEG electrode within about 20 seconds demonstrates effectiveness of the method.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it