Time-frequency analysis of visual evoked potentials by means of matching pursuit with chirplet atoms
Why this work is in the frame
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Bibliographic record
Abstract
Detection of visual evoked potentials (VEP) elicited by repetitive stimuli is valuable in both laboratorial research and clinical practice. Therefore, knowing the characteristics of VEPs is of fundamental importance for adequate design of a signal detector. Usually, the signal is modeled as a steady-state VEP (ssVEP) consisting of the fundamental frequency and the higher harmonics, while ignoring the information contained in its transients (tVEP). We propose here to characterize both tVEP and ssVEP by chirplet time-frequency representation of VEP signal using a matching pursuit (MP) algorithm. Compared to the time-frequency analysis with short-time-Fourier-transform (STFT) and linear-prediction-coding (LPC) method, MP with chirplet shows not only clear characteristics of ssVEP, but a clear spindle-like time-frequency component of tVEP as well, which is not obvious in the other two methods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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