Spectral-Spatial Attention Alignment for Multi-Source Domain Adaptation in EEG-Based Emotion Recognition
Bibliographic record
Abstract
In electroencephalographic-based (EEG-based) emotion recognition, high non-stationarity and individual differences in EEG signals could lead to significant discrepancies between sessions/subjects, making generalization to a new session/subject very difficult. Most existing domain adaptation (DA) and multi-source domain adaptation (MSDA) techniques aim to mitigate this discrepancy by aligning feature distributions. However, when confronted with many diverse domain distributions, learning domain-invariant features via aligning pairwise feature distributions between domains can be hard or even counterproductive. To address this issue, this article proposes an attention alignment approach to learning abundant domain-invariant features. The motivation is simple: despite individual differences causing significant differences in feature distributions in EEG-based emotion recognition, shared affective cognitive attributes (attention) of spectral and spatial domains can be observed within the same emotion categories. The proposed spectral-spatial attention alignment multi-source domain adaptation (S2A2-MSDA) constructs domain attention to represent affective cognition attributes in spatial and spectral domains and utilizes domain consistent loss to align them between domains. Furthermore, to facilitate discriminative feature learning on the target classes, S2A2-MSDA learns the conditional semantic information of the target domain using a pseudo-labeling method. This algorithm has been validated on the SEED and SEED-IV datasets in cross-session and cross-subject scenarios, respectively. Experimental results demonstrate that S2A2-MSDA outperforms existing representative DA and MSDA methods, achieving state-of-the-art 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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".