Resting State EEG Variability and Implications for Interpreting Clinical Effect Sizes
Bibliographic record
Abstract
Resting state electroencephalography (rsEEG) is widely used to investigate intrinsic brain activity, with the potential for detecting neurophysiological abnormalities in clinical conditions from neurodegenerative disease to developmental disorders. When interpreting quantitative rsEEG changes, a key question is: how much deviation from a healthy normal brain state indicates a clinically significant change? Here, we build on the existing rsEEG variability literature by quantifying how this baseline rsEEG range can be attributed to common but underinvestigated sources of variability: experiment day, time of day, and pre-recording exercise level. We found that even within individuals, frequency band powers and entropy measures can vary by 7% (sample entropy and relative alpha power) to 28% (absolute delta power). Absolute and relative delta power increased significantly after running, while relative theta power decreased significantly. Relative beta and gamma power were significantly higher in the afternoon compared to morning trials. Sample entropy and alpha power were relatively consistent. The coefficients of variability we found are similar to some clinical rsEEG effect sizes identified in prior literature, bringing into question the clinical significance of these effect sizes. Furthermore, time of day and activity level accounted for more rsEEG variability than experiment day, indicating the potential to reduce variability by controlling for these factors in repeated-measures studies.
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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 | 0.001 | 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".