MSDA-Net: Multi-source Domain Adaptive Network for Multi-modal Emotion Recognition
Bibliographic record
Abstract
Electroencephalogram (EEG) has shown g reat potential in multi-modal emotion recognition (MER) due to its ability to directly capture emotional states. However, the nonstationarity of EEG signals leads to significant variations across subjects and sessions, posing challenges for subject-independent MER. While previous methods have made significant progress, they often fail to integrate multimodal signals into transfer learning frameworks effectively. To address this limitation, we propose a Multi-source Domain Adaptive Network (MSDA-Net) for MER, designed to mitigate cross-subject and cross-session distribution shifts and enhance recognition performance. Specifically, we first design a feature alignment module to integrate features from different modalities, generating cross-modal feature representations and extracting representative shared features. To further improve generalization, we incorporate domain-specific feature extractors to capture domain-invariant emotional representations. Additionally, we introduce an adapter module to adjust the feature representations between different modalities, aiming to capture inter-individual differences and cross-modal correlations better. Finally, we unify classification loss, discrepancy loss, and maximum mean discrepancy (MMD) loss into a joint optimization framework. Abundant experiments on the SEED and SEED-IV datasets demonstrate the superiority of MSDA-Net, highlighting its effectiveness in improving MER 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".