Ultra-low Noise EEG at LSBB: Effective Connectivity Analysis
Bibliographic record
Abstract
In this study, we further investigate electroencephalographic (EEG) data recorded during October 2014 in the ultra-shielded capsule at LSBB, with a focus on the study of task-specific Granger-causal effective connectivity pat-terns. In previous studies, we showed that noise-free EEG signals acquired in LSBB are suitable for analysis of activity patterns in high frequency bands, i.e. 30 Hz and above. We previously demonstrated that increases in task/rest gamma band (30-70 Hz) energy ratios during ankle and wrist movements are more prominent in the LSBB capsule than in an above-ground hospital environ-ment. The present study extends previous analyses by examining gamma-band connectivity, i.e. the functional patterns of interaction between 64 channels of EEG within the gamma band during motor tasks. We use parameters from a MultiVariate Auto-Regressive (MVAR) model to estimate effective connectivity in 10-second batches of EEG and report the average patterns across all batches in which subjects repetitively move their ankle/wrist. We report the gamma-band connectivity results in a reduced form as strength of hemispheric and inter-regional connections. The analysis reveals that for some subjects, significant channel-wise connections in the LSBB capsule outnumber those in the hospital, suggesting that patterns of gamma-band connectivity are better reflected in low-noise environments. This study again demonstrates the poten-tial of the ultra-shielded capsule and motivates further protocol enhancements and analysis methods for conducting future high-frequency EEG studies within LSBB.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".