EEG-Based Machine Learning for Emotional Stress Recognition in the Valence–Arousal Space
Bibliographic record
Abstract
Emotional stress impacts mental health and cognitive function, influencing human performance by affecting memory and attention.This study generated its own dataset of electroencephalographic (EEG) signals recorded using a Brain-Computer Interface (BCI) device.Participants were induced to stress using math tasks with strict time constraints.This dataset was used to identify significant features for the detection of emotional stress.The EEG signals were labeled according to their respective positions on the valence-arousal plane.Significant quadrant-specific thresholds relevant to stress were determined for classification and subsequent analysis.The class imbalance was mitigated using resampling methods.Feature extraction was performed using techniques in time, frequency, and timefrequency domains for obtaining a comprehensive signal representation.Principal Component Analysis (PCA) was applied to the extracted features to reduce dimensionality and improve model generalization.The features served as inputs to various CNN architectures to identify the optimum models for recognizing stress.The best recognition accuracy of 90.2% was obtained in the recognition of stress-related emotional states.The findings demonstrate the effectiveness of the combination of EEG signal processing and machine learning algorithms in the detection of stress levels in the valence-arousal emotional space.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".