Spectral-Spatial Attention Alignment for Multi-Source Domain Adaptation in EEG-Based Emotion Recognition
Why this work is in the frame
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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 (S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>A<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>-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, S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>A<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>-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 S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>A<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>-MSDA outperforms existing representative DA and MSDA methods, achieving state-of-the-art performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it