Single channel analysis of electromagnetic brain signals through ICA in a dynamical systems framework
Why this work is in the frame
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Bibliographic record
Abstract
This paper introduces a method for extracting information from single channel recordings of electromagnetic (EM) brain signals. In a dynamical embedding framework, the measured electroencephalogram (EEG) and magnetoencephalogram (MEG) signals are assumed generated by the non-linear interaction of a few degrees of freedom. In a three-step process, first an appropriate embedding matrix is constructed out of a series of delay vectors from the measured signal. Then independent component analysis (ICA) is performed on the embedding matrix to decompose the single channel recording into its underlying independent components (ICs). The ICs are treated as a convenient expansion basis and subjective methods are then used to identify components of interest relevant to the application. These ICs are then projected back onto the measurement space in isolation. The method has been applied to single channels of both EEG and MEG recordings and is shown to isolate, amongst others: (i) artifactual components such as ocular, electrocardiographic and electrode artifact, (ii) seizure components in epileptic EEG recordings and (iii) theta band, tumour related, activity in MEG recordings.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it