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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".