The modulatory effect of transcutaneous electrical nerve stimulation (TENS) on tonic heat pain in the human brain: topographic mapping of absolute EEG power spectra
Bibliographic record
Abstract
Objective To investigate the effect of transcutaneous electrical nerve stimulation (TENS) on baseline eyes-closed brain activation and on pain-related EEG activity when TENS is applied contralatarally to the hand of pain. Method 128-channel electroencephalography (EEG) was recorded in four conditions including baseline,heat pain test,TENS stimulation and contralateral TENS modulation in 15 right-handed healthy young males. The Short Form McGill Pain Questionnaire was employed to assess pain sensation. Fast Fourier Transformation analysis (FFT) was performed to calculate absolute EEG power spectra based on 7 bands spectrum. Results ① The fronto-central beta-1 activity was significantly enhanced by TENS compared to eyes-closed [eyes-closed:(290±201)μV2,TENS:(385±224)μV2,t=3.323,P<0.01].②TENS markedly relived pain sensation assessed by SF-MPQ and significantly increased the posterior-anterior alpha-1 power [pain:(332±221)μV2,TENS modulation:(378±243)μV2,t=3.683,P<0.01] and fronto-central and posterior beta-1 activities [pain:(348±213)μV2,TENS modulation:(397±240)μV2,t=2.362,P<0.05]. Conclusion TENS,used as a peripheral nerve stimulation,mainly activates sensorimotor cortex.The increased Alpha-1 activity implies that TENS is likely able to relieve pain affective. Key words: TENS; Tonic heat Pain; Absolute EEG power spectra
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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 | 0.000 |
| 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) | 0.002 | 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".