Using Phase Shift Granger Causality to Measure Directed Connectivity in EEG Recordings
Bibliographic record
Abstract
Cortical activity is maintained by neural networks working in tandem. Electroencephalographic (EEG) signals across two sites are said to be coherent with one another when they show consistent phase relations. However, periods of desynchrony beginning with a shift in phase relations are a necessary aspect of information processing. Traditional measures of EEG coherence lack the temporal resolution required to divide the relationship between two signals into periods of synchrony and desynchrony and are unable to specify the direction of information transmission (i.e., which site is leading and which is lagging), a goal referred to as directed connectivity. In this article, the authors introduce a novel method of measuring directed connectivity by applying the framework of Granger causality to phase shift events which are estimated with high temporal resolution. A simulation study is used to verify that the proposed method is able to identify connectivity patterns in situations similar to EEG recordings, such as high levels of noise and linear source mixing. Their method is able to correctly identify both the existence and direction of information transfer, and that the existence of spatiotemporal noise serves to reduce the spread of shift identification due to volume conduction. To demonstrate the method on real data, it is applied to EEG recordings from 18 adolescents during a resting period and auditory and visual vigilance tasks. Their new measure, Phase Shift Granger Causality (PSGC), is able to clearly distinguish between the resting task and the active tasks. The latter have higher rates of connectivity overall and specifically more long-range connections. As expected, the resting task appears to activate more localized neural circuitry, whereas the active tasks appear to increase communication across several neural regions involved in vigilance tasks. The vigilance tasks also showed significantly higher clustering coefficients than the resting task, a property associated with small-world networks.
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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 | 0.002 | 0.020 |
| 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) | 0.000 | 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".