Alpha suppression during prehension indicates neural motor drive inhibition
Bibliographic record
Abstract
Abstract Changes in alpha band activity (8-12 Hz) have been shown to indicate the inhibition of engagement of brain regions during cognitive tasks, reflecting real-time cognitive load. Despite this, its feasibility to be used in a more dynamic environment with ongoing motor corrections has not been studied. This research used electroencephalography (EEG) to explore how different brain regions are engaged during a simple grasp and lift task where unexpected changes to the object’s properties are introduced. To our knowledge, this is the first study to show alpha activity changes related to motor error correction occur only in motor-related areas (i.e. central areas), but not in error processing areas (ie. fronto-parietal network). This suggests that oscillations over motor areas could reflect inhibition of motor drive related to motor error correction, thus being a potential cortical electrophysiological biomarker for the process, and not solely as a proxy for cognitive demands. This observation is particularly relevant in scenarios where these signals are used to evaluate high cognitive demands co-occurring with high levels of motor errors and corrections, such as prosthesis use. The establishment of electrophysiological biomarkers of mental resource allocation during movement and cognition can help identify indicators of mental workload and motor drive, which may be useful for improving brain-machine interfaces. New and Noteworthy This research expands on previous fMRI literature by demonstrating that alpha band suppression, an EEG metric with high temporal resolution, occurs over the primary sensorimotor area during error correction of hand movements. This furthers our understanding of alpha suppression beyond processes related to cognitive demands by highlighting how motor control also influences this frequency band. Recognizing that alpha band activity is modulated by both motor and cognitive processes is important in situations where high cognitive demands can lead to a high level of movement errors. Interpretations of such modulation are often attributed only to cognitive demands, whereas a motor process may also play a factor. Furthermore, alpha suppression could be used as a biomarker for error correction with applications in human machine interfaces, such as neuroprostheses.
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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) | 0.002 | 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".