Identification of Beta Oscillatory Patterns During a Hand Grip Motor Task: A Comparative Analysis pre- and post-Exercise
Bibliographic record
Abstract
Electroencephalography (EEG) based Movement-Related Beta Band Desynchronization (MRBD) within the beta frequency band (13 - 30Hz) is commonly observed during motor task execution, and it has been associated with motor task performance. More recently, transient burst-like events termed beta bursts have been identified as another potential biomarker of motor function. Previous studies have reported decreased MRBD magnitude induced by exercise. However, little is known in terms of the effects of high-intensity exercise on beta burst patterns. In the present work, we investigated the modulatory effects of exercise on different beta burst features prior to, during and post motor task execution. We found that exercise mainly affected burst duration and burst rate within the left motor cortex area (M1) that is contralateral to the moving hand. Meanwhile, burst amplitude in the contralateral M1 area was affected differently by exercise, with smaller burst amplitude values observed during the movement preparation phase and larger magnitude during as well as post motor task execution. Since MRBD and beta burst patterns are closely associated with motor task performance, results from the present study can promote understanding about the association between exercise induced neural plasticity changes and motor performance, which can be further used for designing a subject-specific training therapy for improving motor function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".