Understanding Power of Graph Convolutional Neural Network on Discriminating Human EEG Signal
Bibliographic record
Abstract
Electroencephalogram (EEG) based biometric is an emerging field in security systems, which provides a higher reliability compared to the conventional identification methods. EEG devices capture the temporal brain waves of individuals that are unique and cannot be reproduced. The individual variability of EEG signals can be derived from its functional connection, which is best represented by a graph. Although a Graph Convolutional Neural Network (GCNN) has improved the classification of graph data in recent years, GCNN is still not well addressed for the EEG biometric identification system. We introduce a novel GCNN model that overcomes the lack of performance on small graph data. Furthermore, we investigate the performance of recent GCNN benchmarks considering both model loss and complexity. The proposed GCNN results in more that 95 % accuracy with considerably low computational cost for 3 databases recorded in 8 different human states. Our experiment shows that adding an extra graph convolution or pooling layer does not necessarily result in better performance.
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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 | 0.002 |
| 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) | 0.001 | 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".