Resting EEG is affected by exposure to a pulsed ELF magnetic field
Bibliographic record
Abstract
An increasing number of reports have demonstrated a significant effect of extremely low frequency magnetic fields (ELF MFs) on aspects of animal and human behavior. Recent studies suggest that exposure to ELF MFs affects human brain electrical activity as measured by electroencephalography (EEG), specifically within the alpha frequency (8-13 Hz). Here we report that exposure to a pulsed ELF MF with most power at frequencies between 0 and 500 Hz, known to affect aspects of analgesia and standing balance, also affects the human EEG. Twenty subjects (10 males; 10 females) received both a magnetic field (MF) and a sham session in a counterbalanced design for 15 min. Analysis of variance (ANOVA) revealed that alpha activity was significantly higher over the occipital electrodes (O1, Oz, O2) [F(1,16) = 6.858; P =.019, eta2 = 0.30] and marginally higher over the parietal electrodes (P3, Pz, P4) [F(1,16) = 4.251; P =.056, eta2 = 0.21] post MF exposure. This enhancement of alpha activity was transient, as it marginally decreased over occipital [F(1,16) = 4.417; P =.052; eta2 = 0.216] and parietal electrodes [F(1,16) = 4.244; P =.056; eta2 = 0.21] approximately 7 min after MF exposure compared to the sham exposure. Significantly higher occipital alpha activity is consistent with other experiments examining EEG responses to ELF MFs and ELF modulated radiofrequency fields associated with mobile phones. Hence, we suggest that this result may be a nonspecific physiological response to the pulsed MFs.
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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 | 0.000 |
| 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) | 0.002 | 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 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".