Bibliographic record
Abstract
We present a methodology for the analysis of electromagnetic (EM) brain signals. In a dynamical systems framework we assume that the measured electroencephalogram (EEG) and the magnetoencephalogram (MEG) are generated by the non-linear interaction of a few degrees of freedom. Within this framework, we then construct an embedding matrix, which consists of a series of consecutive delay vectors. The embedding matrix describes a trajectory on the Euclidean manifold recreating the unobservable system manifold, which is assumed to be generating the measured data. The embedding matrix can be used to quantify system complexity, which changes with the changes in brain-'state'. To this end, we use measures of entropy and Fisher's information measure to track changes in complexity of the system over time. It is also possible to perform Independent Component Analysis on the embedding matrix to decompose the single channel recording into a set of underlying independent components. The independent components are treated as a convenient expansion basis and subjective methods are used to identify components of interest relevant to the application at hand. The method is applied to just single channels of both EEG and MEG recordings and is shown to give intuitive and meaningful results in a neurophysiological setting.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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".