Entropy in Electroencephalographic Signals Modulates with Force Magnitude During Grasping – A Preliminary Report
Bibliographic record
Abstract
The ability to hold objects relies on neural processes underlying grip force control during grasping. Brain activity lateralized to contralateral hemisphere averaged over trials is associated with grip force applied on an object. However, the involvement of neural variability within-trial during grip force control remains unclear. We examined dependence of neural variability over frontal, central, and parietal regions of interest (ROI) on grip force magnitude using noninvasive electroencephalography (EEG). We utilized our existing EEG dataset comprised of healthy young adults performing an isometric force control task, cued to exert 5, 10, or 15% of their maximum voluntary contraction (MVC) across trials and received visual feedback of their grip force. We quantified variability in EEG signal via sample entropy (sequence-dependent) and standard deviation (sequence-independent measure) over ROI. We found lateralized modulation in EEG sample entropy with force magnitude over central electrodes but not over frontal or parietal electrodes. However, modulation was not observed for standard deviation in the EEG activity. These findings highlight lateralized and spatially constrained modulation in sequence-dependent, but not sequence-independent component of EEG variability. We contextualize these findings in applications requiring finer precision (e.g., prosthesis), and propose directions for future studies investigating role of neural entropy in behavior.
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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 | 0.002 |
| 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".