Capturing with EEG the Neural Entrainment and Coupling Underlying Sensorimotor Synchronization to the Beat
Bibliographic record
Abstract
Synchronizing movements with rhythmic inputs requires tight coupling of sensory and motor neural processes. Here, using a novel approach based on the recording of steady-state-evoked potentials (SS-EPs), we examine how distant brain areas supporting these processes coordinate their dynamics. The electroencephalogram was recorded while subjects listened to a 2.4-Hz auditory beat and tapped their hand on every second beat. When subjects tapped to the beat, the EEG was characterized by a 2.4-Hz SS-EP compatible with beat-related entrainment and a 1.2-Hz SS-EP compatible with movement-related entrainment, based on the results of source analysis. Most importantly, when compared with passive listening of the beat, we found evidence suggesting an interaction between sensory- and motor-related activities when subjects tapped to the beat, in the form of (1) additional SS-EP appearing at 3.6 Hz, compatible with a nonlinear product of sensorimotor integration; (2) phase coupling of beat- and movement-related activities; and (3) selective enhancement of beat-related activities over the hemisphere contralateral to the tapping, suggesting a top-down effect of movement-related activities on auditory beat processing. Taken together, our results are compatible with the view that rhythmic sensorimotor synchronization is supported by a dynamic coupling of sensory and motor related activities.
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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 | 0.000 |
| 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".