Power spectral analysis of resting-state EEG to monitor psychological resilience to stress
Bibliographic record
Abstract
Psychological resilience refers to an individual's capacity to adapt and recover from challenging situations, stress, or traumatic events. However, there is currently no universally accepted standard for assessing resilience in research, leading heterogeneity in diverse approaches and measures across different studies. Thus, the present study aimed to test the hypothesis that spectral analysis of the resting-state electroencephalograms (EEG) can be correlated to resilience scores, and ultimately used as the standard method of measuring psychological resilience of patients. We recorded the Perceived Stress Scale (PSS) and the Social Readjustment Rating Scale (SRRS) scores of 299 Canadian participants recruited at the Centre for Addiction and Mental Health, an academic hospital affiliated with the University of Toronto. The PSS and SRRS scores were used to generate a regression model to utilize residuals as a measure of resilience to stress. Resting-state EEG data was recorded from 55 healthy subjects extracted from the total sample, and the relative power spectrum of 8 EEG electrodes (F3, F4, C3, C4, P3, P4, O1, O2) for each frequency band (delta, theta, alpha, and beta) were calculated to compare with the resilience scores was tested using the Pearson coefficient. A significant positive correlation between PSS scores and SRRS scores was identified. The EEG power spectrum analysis did not yield any significant findings, except for a trend towards significance in the theta band at electrode P4 (p-value = 0.081). Therefore, the results provide a limited possibility of utilizing EEG to measure psychosocial resilience.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 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 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".