Establishing a Right Frontal Beta Signature for Stopping Action in Scalp EEG: Implications for Testing Inhibitory Control in Other Task Contexts
Bibliographic record
Abstract
Many studies have examined the rapid stopping of action as a proxy of human self-control. Several methods have shown that a critical focus for stopping is the right inferior frontal cortex. Moreover, electrocorticography studies have shown beta band power increases in the right inferior frontal cortex and in the BG for successful versus failed stop trials, before the time of stopping elapses, perhaps underpinning a prefrontal-BG network for inhibitory control. Here, we tested whether the same signature might be visible in scalp electroencephalography (EEG)-which would open important avenues for using this signature in studies of the recruitment and timing of prefrontal inhibitory control. We used independent component analysis and time-frequency approaches to analyze EEG from three different cohorts of healthy young volunteers (48 participants in total) performing versions of the standard stop signal task. We identified a spectral power increase in the band 13-20 Hz that occurs after the stop signal, but before the time of stopping elapses, with a right frontal topography in the EEG. This right frontal beta band increase was significantly larger for successful compared with failed stops in two of the three studies. We also tested the hypothesis that unexpected events recruit the same frontal system for stopping. Indeed, we show that the stopping-related right-lateralized frontal beta signature was also active after unexpected events (and we accordingly provide data and scripts for the method). These results validate a right frontal beta signature in the EEG as a temporally precise and functionally significant neural marker of the response inhibition process.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".