Evaluating Cultural Impact on Subject-Independent EEG-Based Emotion Recognition Across French, German, and Chinese Datasets
Bibliographic record
Abstract
Culture influences emotional expression and recognition, affecting how individuals perceive and regulate emotions. Given this effect of cultural background, previous studies have suggested that incorporating demographic information can enhance emotion recognition in Electroencephalography (EEG) based approaches. However, until now, most studies have focused on improving prediction accuracy, ignoring the extent to which cultural factors impact EEG-based emotion recognition. To address that gap, this study investigates how cultural factors impact emotion prediction by using a stacking model that combines attention mechanism layers with multinomial logistic regression. The attention mechanism layer focused on detecting the cortical areas in which the model paid more attention to predicting the emotions, while the logistic regression analyzed how the cultural factors affect the odds of accurately predicting emotions. To test our model, we used EEG data capturing three emotions (negative, neutral, and positive) from 31 subjects of three nationalities: 15 Chinese, 8 French, and 8 German. Our approach achieved accuracies of 77.3%, 73%, and 65% for recognizing the emotions in the Chinese, French, and German subjects, respectively. Our approach revealed that incorporating cultural information increases the odds of predicting positive emotions for Chinese subjects and negative emotions for French and German subjects. Moreover, French and German subjects exhibited similar neural patterns across emotions, indicating a closer cultural alignment between these groups. Our findings highlight the critical role of cultural context in emotion recognition models. This inclusion not only improves emotion prediction accuracy for subject-independent approaches but also promotes inclusivity and ethical practices in emotion recognition systems.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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; both teacher heads agree on what is shown here.
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".