Transformer-Based Bimodal Emotion Recognition: Signal Measurement and Transformer Fusion Approach
Bibliographic record
Abstract
Human emotion plays a fundamental role in cognition, decision-making, and social interactions. Electroencephalography (EEG) is a fundamental widely used neurophysiological measurement tool that records brain electrical activity, providing valuable insights into emotional states. This paper presents a novel transformer-based bimodal emotion recognition model that integrates EEG and facial expression data to enhance multimodal representation learning and emotion classification accuracy. A comprehensive evaluation is conducted by implementing and benchmarking various state-of-the-art unimodal and bimodal emotion recognition models as well as exploring multiple fusion techniques. The model’s performance is validated on two widely used datasets, DEAP and MAHNOB-HCI, where the proposed model outperforms existing approaches in both unimodal and bimodal configurations, achieving an approximate 4% higher accuracy for valence and arousal dimensions. Specifically, for the DEAP dataset, the model attains classification accuracies of 0.66 (valence) and 0.72 (arousal) on the training set and 0.65 (valence) and 0.66 (arousal) on the test set. For the MAHNOB-HCI dataset, it achieves 0.69 (valence) and 0.73 (arousal) on the training set and 0.61 (valence) and 0.68 (arousal) on the test set. Additionally, the proposed transformer-based fusion method outperforms traditional feature-level and decision-level fusion techniques.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".