EEG Signals Classification Using Novel Acquisition Protocol for Lie Detection System
Bibliographic record
Abstract
Lie detection is a well-known word that refers to one person acting in such a way that the other person believes something that is incorrect.Lie detection plays a sensitive part in various Scope including, national security, law enforcement, and psychology.To address this issue, lie detection has received a lot of interest lately.In this research, a deep learning algorithm with a new dataset and protocol is employed to automatically detect truth from electroencephalography (EEG) data.This experiment utilized the OpenBCI Ultracortex "Mark IV" EEG Headset, which acquired 14 channel of EEG data from ten participant.The acquired signal was pre-processing and then inputted individually into three classifiers-MLP, LSTM, and CNN-in order to distinguish between honest or guilty statements in the EEG data and also select the model with the best performance.The indicated manner is non-surgical, effective, and powerful, with least time complication, consequently appropriate for real-time applications.To implement the experiment on EEG signal for deceit detection, a novel dataset and protocol based on video was created.In addition, we compared the outcomes of our method to an existing dataset called Dryad Dataset, which used image protocol.The finding of the proposed system is evaluated using various measures such as accuracy, F1 score, recall, and precision.According to the testing outcomes, the CNN technique achieves the highest incredible accuracy of 99.96% on the EEG data set in our dataset and 99.36% on the Dryad dataset.Finally, the suggested system provides impressive results in comparison with existent algorithms presented in the literature and is precise, scalable, and fault-tolerant.
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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) | 0.001 | 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) | 0.004 | 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".