Time-resolved functional connectivity from high-density EEG for characterizing the level of consciousness in behaviorally unresponsive patients
Bibliographic record
Abstract
Brain-computer interfaces (BCIs) have shown enormous promise in the detection of consciousness in minimally responsive individuals. To date, most BCIs have relied on the presence of high-level cognitive abilities (e.g. attention, language comprehension) in non-responsive individuals, resulting in a large number of cases of undetected - or covert - consciousness. An alternate approach is to measure the underlying properties of brain networks, which makes no assumptions about the presence of certain cognitive capacities. Brain networks can be represented through functional connectivity of different brain areas. To date, the vast majority of studies have used time-averaged functional connectivity to represent a state of consciousness. In this paper, we compare time-averaged versus time-resolved functional connectivity, and the information contained by each in different states of consciousness. We present a novel analysis to evaluate the dynamic properties of time-resolved, high-resolution estimates of phase-based functional connectivity using weighted phase lag index (wPLI) calculated from high-density EEG. In a case study of two individuals in disorders of consciousness, we demonstrate that time-resolved functional connectivity reflects the dynamic properties of brain networks, providing more information about an individual's state of consciousness than traditional time-averaged approaches. Our findings support time-resolved functional connectivity as the basis for a passive BCI with the potential to characterize the level of consciousness in behaviourally unresponsive patients.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| 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".