Identification of beta burst patterns underlying simultaneous transcranial alternating current stimulation
Bibliographic record
Abstract
Abstract Introduction: Beta bursts represent transient high-power brain waves within the beta frequency band. Previous studies have reported that beta bursts are closely associated with motor function [1]. Moreover, transcranial alternating current stimulation (tACS), a non-invasive brain stimulation technique, has been reported to be able to improve motor performance [2]. However, how tACS modulate beta burst patterns is still unclear. Therefore, the objective of the present study is to investigate changes in beta burst patterns underlying simultaneously applied tACS. Method: The dataset we used was provided by Dr. Noury from University of Tuebingen [3]. We used EEG recordings underlying 62Hz tACS for data analysis. To extract beta burst, we first band-pass filtered the EEG data within the beta frequency band (13 – 30Hz). Then, 75 percent of the average envelope of EEG signals under the sham stimulation condition was used as the threshold for burst detection. Finally, three recording electrodes covering the right motor cortex area and three burst features were analyzed to investigate the modulatory effects of gamma band tACS on beta burst patterns: burst number per trial, burst duration and burst amplitude. Results: During 62Hz tACS, more bursts along with larger burst amplitude were detected compared to the sham group (Figure 1A & C). However, duration of burst was reduced by 62Hz tACS (Figure 1B). Conclusion: In the present study, we investigated the changes in burst patterns underlying simultaneous 62Hz tACS. We showed that gamma band tACS can induced more bursts with larger amplitudes, though these bursts tend to be of shorter duration. This may imply that gamma tACS can excite more neurons to the firing state with larger spikes. Keywords: tACS, EEG, Beta burst, Motor function
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".