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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".