Robust Emotion Recognition in EEG Signals Based on a Combination of Multiple Domain Adaptation Techniques
Bibliographic record
Abstract
Conventional classification approaches for EEG- based emotion recognition cannot often adapt to different domains, such as cross-subject or cross-dataset scenarios, leading to poor performance. To handle this challenge, we introduce a novel fusion method using a combination of multiple domain adaptation techniques to improve the emotional states in EEG datasets via classification accuracy. For this aim, Our proposed approach exploits domain adaptation approaches such as Transfer Component Analysis (TCA), Correlation Alignment (CORAL), Transfer Joint Matching (TJM), Geodesic Flow Kernel (GFK), and Joint Distribution Adaptation (JDA), to enhance the overall classification performance. Later, a new fusion approach called Multiple Domain Adaptation based on a Neuro-Fuzzy Inference System (MDA-NF) is applied to combine the classifiers using proper fuzzy membership functions and deliver maximum separation between classes. The main contribution is by applying the fusion approach using MDA- NF technique, adaptability is sufficiently enhanced. Another advantage is to employ multiple adaptation techniques that improve separation between classes. In experimental test results conducted with cross-subject and cross-dataset scenarios, the MDA-NF approach demonstrates superior performance in terms of accuracy for both the valence and arousal aspects, as observed in two public DEAP and DREAMER datasets.
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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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".