Stress Assessment by Prefrontal Relative Gamma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Stress assessment has been under study in the last years. Both biochemical and physiological markers have been used to measure stress level. In neuroscience, several studies have related modification of stress level to brain activity changes in limbic system and frontal regions, by using non-invasive techniques such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). In particular, previous studies suggested that the exhibition or inhibition of certain brain rhythms in frontal cortical areas indicates stress. However, there is no established marker to measure stress level by EEG. In this work, we aimed to prove the usefulness of the prefrontal relative gamma power (RG) for stress assessment. We conducted a study based on stress and relaxation periods. Six healthy subjects performed the Montreal Imaging Stress Task followed by a stay within a relaxation room while EEG and electrocardiographic signals were recorded. Our results showed that the prefrontal RG correlated with the expected stress level and with the heart rate (HR) (0.8). In addition, the difference in prefrontal RG between time periods of different stress level was statistically significant (p-value < 0.01). Moreover, the RG was more discriminative between stress levels than alpha asymmetry, theta, alpha, beta, and gamma power in prefrontal cortex. We propose the prefrontal RG as a marker for stress assessment. Compared with other established markers such as the HR or the cortisol, it has higher temporal resolution. Additionally, it needs few electrodes located at non-hairy head positions, thus facilitating the use of non-invasive dry wearable real-time devices for ubiquitous assessment of stress.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it