Time–Frequency Phase Analysis of Ictal EEG Recordings With the S-Transform
Bibliographic record
Abstract
The calculation and visualization of temporal and phase information in the brain, such as during cognitive processes and epileptiform activity, is an important tool in EEG-based studies of physiological brain activation. To this end, we present a technique that estimates the phase and time offsets between different channels in EEG recordings of seizure activity. The offset information is visually combined with amplitude information to emphasize the most significant signal features. The estimates of phase and time offset are derived from the S-transform, a time-frequency representation that is similar to a windowed Fourier transform, but with a wavelet-like, scalable window. The phase offsets are obtained from the differences between phase spectra of S-transforms of different traces, and the time offsets are then obtained from the frequency-domain gradients of the phase offsets. This is analogous to the link between frequency "phase ramping" and time translation in ordinary Fourier analysis. In this paper, we present a synthetic example to help describe the method, and then show ictal EEG recordings from two human subjects. The differences between the recording times of spike-wave discharges at different electrodes exhibit behavior that is strongly dependent on time and frequency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".