Impact of sex differences on subject-independent EEG-based emotion recognition models
Bibliographic record
Abstract
Electroencephalography (EEG) can capture emotion regulation by recording electrical activity from the brain cortex. This 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 since it does not require recalibration for new users. However, it faces challenges due to the high variability of EEG signals among individuals. Recent studies suggest that incorporating subjects’ sex can enhance the accuracy of subject-independent models due to differences in emotional processing between males and females. Although previous studies have demonstrated the effect of sex on emotion recognition, they have primarily focused on the predictive aspect, neglecting the interpretability of how emotion regulation differs between males and females. This work addresses this limitation by using attention network layers to identify brain areas more involved in predicting emotions. Additionally, an odds ratio analysis was conducted using logistic regression to evaluate the impact of sex on emotion prediction. Our findings reveal that cortical activation patterns elicited by emotional audio-visual stimuli differ between females and males, with females showing more neural activation in the left hemisphere and males showing more in the right hemisphere. Moreover, when the output probabilities of the deep learning models are further postprocessing with the subject’s sex, the odds of correctly predicting emotions increase. These findings suggest that sex differences can lead to more robust subject-independent emotion recognition models. • Audio-visual stimuli elicit distinct brain activation patterns in females and males. • Females exhibit greater activation in the left hemisphere during emotion processing. • Males display higher activation in the right hemisphere during emotion processing. • Incorporating biological sex enhances emotion prediction accuracy in subject-independent approaches.
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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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".