Evaluating cultural impact on subject-independent EEG-based emotion recognition approaches
Bibliographic record
Abstract
Culture plays a crucial role in shaping emotional expression and recognition, influencing how individuals perceive and regulate emotions. Electroencephalography (EEG) can capture electrical activity associated with human emotion processing from the scalp. The electrical activity can be processed using deep learning models to predict emotional states. Two approaches can be employed to develop these deep learning models: subject-dependent and subject-independent. The subject-independent approach is more practical as it trains the model on data from some individuals and tests it on entirely different individuals, ensuring it generalizes well to new users. However, because of the high variability of EEG across individuals, the subject-independent approach tends to yield low performance. Recent studies suggest incorporating demographic information along with EEG signals is one way to overcome this issue. By using the subject-independent approach, this study investigates how cultural factors impact emotion prediction. Specifically, we used a stacking model that combines deep learning with multinomial logistic regression to predict positive, neutral, and negative emotions among 15 Chinese, 8 French, and 8 German subjects. Our approach achieved accuracies of 77.3% for Chinese subjects, 73% for French subjects, and 65% for German subjects, which are comparable to or exceed accuracies reported by previous studies. Our approach highlighted that incorporating cultural information increases the likelihood of predicting positive emotions for Chinese participants and negative emotions for Europeans. Moreover, French and German subjects exhibited similar neural patterns across all emotions, suggesting a more common cultural sharing between those subjects. Overall, our findings emphasize the importance of integrating cultural considerations into 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.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".