[Analysis of the effect of neuromuscular electrical stimulation on corticomuscular coupling during standing balance].
Bibliographic record
Abstract
Neuromuscular electrical stimulation (NMES) has been proven to promote human balance, but research on its impact on motor ability mainly focuses on external physical analysis, with little analysis on the intrinsic neural regulatory mechanisms. This study, for the first time, investigated the effects of NMES on cortical activity and cortico-muscular functional coupling (CMFC) during standing balance. Twelve healthy subjects were recruited in bilateral NMES training, with each session consisting of 60 electrically induced isometric contractions. Electroencephalogram (EEG) signals, electromyogram (EMG) signals, and center of pressure (COP) signals of the foot sole were collected before stimulation, two weeks after stimulation, and four weeks after stimulation while the subjects maintained standing balance. The results showed that NMES training improved subjects' postural stability during standing balance. Additionally, based on the EMG power spectral density (PSD), the κ frequency band was defined, and EEG-EMG time-frequency maximal information coefficients (TFMIC) were calculated. It was found that NMES enhanced functional connectivity between the cortex and lower limb muscles, with varying degrees of increase in β-κ and γ-κ frequency band CMFC after stimulation. Furthermore, sample entropy (SE) of EEG signals also increased after training. The results of this study confirm that NMES training can enhance CMFC and brain activation during standing balance. This study, from the perspective of physiological electrical signals, validates the effectiveness of NMES for balance training and provides objective assessment metrics for the training effects of NMES.
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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 | 0.001 | 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".