CoAdapt: Collaborative Adaptation Between Latent EEG Feature Representation and Annotation for Emotion Decoding
Bibliographic record
Abstract
Electroencephalogram (EEG) data contains rich neurophysiological information that can objectively express the emotional state of human beings. However, the inherent EEG characteristics such as non-stationarity and weakness, combined with the possible limited immersion and carry-over effect of subjects during data-collection experiments, may cause that the semantic meaning of extracted EEG feature vector cannot well match its annotated emotional state, dubbed the ‘feature-label inconsistency dilemma in EEG-based emotion decoding. To this end, this paper proposes to alleviate the side effect of ‘feature-label inconsistency from both feature and label aspects. On one hand, we explore more meaningful emotion-related EEG representation by the latent low-rank representation. On the other hand, enhance the correspondence between explored EEG representation and its annotated emotional state by a label dragging strategy. As a result, a collaborative adaptation (CoAdapt) model between latent EEG feature representation and its annotation is formed for efficient emotion decoding, which is implemented within the semi-supervised framework to better capture the properties of both the labeled and unlabeled EEG data. Experimental results on three publicly available datasets, SEED-IV, SEED-V and MPED, depict that 1) CoAdapt achieves better emotion recognition performance in comparison with some related models; 2) the improvements of inter-class separability and label margin are empirically evaluated, indicating the effectiveness of the purified EEG feature representation and rectified emotion annotation; 3) some task-related results are identified from data-driven perspective, including the emotion carry-over effect and the discriminative spatial patterns in emotion decoding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".