DM-EEGID: EEG-Based Biometric Authentication System Using Hybrid Attention-Based LSTM and MLP Algorithm
Bibliographic record
Abstract
Owing to the reliability of biometric data, person identification systems are developed using many different biometric data.However, these systems can be easily fooled with prosthetic face masks, contact lenses and fingerprint tapes.EEG signal is considered to be the most difficult biometric data to copy.The main reason why EEG-based identification systems have not become widespread is that their accuracy performance is not stable.In this study, an EEG based identification system DM-EEGID with improved accuracy performance is proposed.In this approach, first of all, the channels that are meaningless and reduce the accuracy performance from the high number of channels used as input data should be filtered out.Therefore, a Random Forest based binary feature selection method is recommended.With this algorithm, it has been determined that the optimum number of channels for the highest percentage of accuracy in the 64-channel data set is 48-channel.Then, for the determination of the most distinctive frequency subcomponent, the delta pattern was determined to be the most appropriate frequency component by inter-section correlation coefficient analysis.Finally, the proposed approach was tested with hybrid Attention-based LSTM-MLP supported by optimum parameters with both eyes closed and eyes open resting state EEG recording.The proposed model reached 99.96% and 99.70% accuracy percentages for eyes-closed and eyes-open datasets, respectively.These results show that this proposed approach has the potential to be applied in closed systems where the number of people is limited.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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 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".