Bipartite Graph Adversarial Network for Subject-Independent Emotion Recognition
Bibliographic record
Abstract
Emotions play a vital role in connecting and sharing with others. However, individuals with emotional disorders face challenges in expressing their emotions, affecting their social lives. Current artificial intelligence tools support this problem by enabling the development of methods that recognize emotions from electroencephalographic (EEG) signals. However, the high variability across individuals poses challenges in developing emotion recognition methods that generalize well across different subjects. Previous studies have addressed this issue using domain adversarial neural networks (DANN), in which differences in EEG among individuals are minimized. Although DANN has shown a potential to reduce domain variance, previous studies have little explored the inclusion of layer-specific components to further advance towards that goal. This study addressed this limitation by incorporating bipartite (BP) graphs in a DANN architecture to reduce variability further. We evaluated our model on five benchmark datasets for emotion recognition (SEED, SEED-IV, SEED-V, SEED-FRA, and SEED-GER) comprising a total of 62 individuals. Our model yielded an accuracy of 82.1%, 77.3%, 85.8%, 90.7%, and 87.6% for the SEED-V, SEED-IV, SEED, SEED-FRA, and SEED-GER datasets, respectively. Notably, these accuracies are either higher or comparable to the current state-of-the-art models. Furthermore, our model identified that the frontal, temporal, and parietal EEG channels are crucial for detecting emotions evoked by audiovisual stimuli.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".