Bibliographic record
Abstract
Currently the study of affective computing (AC) includes a focus on researching emotion regulation and recognition. Recent studies in this field have utilized deep learning architectures to enhance emotion recognition from EEG signals. An alternative approach to deep learning is to use feature engineering to extract relevant features to train supervised machine learning models. Current theories in the neuroscience field can guide this feature engineering process. Neuroscientists have suggested various models to clarify how emotions are processed. One of these models suggests that positive emotions are processed in the left hemisphere, while negative emotions are processed in the right hemisphere. This emotional processing model has inspired previous studies to propose asymmetrical features to predict emotions. However, none of these studies have statistically evaluated whether the inclusion of asymmetrical features could yield benefits such as increased accuracy or reduced training time. To address that direction, this research presents both statistical evaluations for emotion regulation and a comparable model for emotion recognition. The outcomes show that brain hemispheres and frequency bands participate differently in processing emotions and observed the presence of the two asymmetry emotion processing models but in different frequency ranges. Also, the results from this study imply that by using asymmetry EEG, emotion recognition approaches can use fewer features without significantly compromising performance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".