Windowing Compensation in Fourier Based Surrogate Analysis and Application to EEG Signal Classification
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Bibliographic record
Abstract
This article shows how adding a second step of windowing after each phase randomization can reduce the false rejection rate in the Fourier-based surrogate analysis (SA). Windowing techniques reduce the discontinuities at the boundaries of the periodically extended data sequence in the Fourier Series. However, they add time-domain nonstationarity that affects the SA. This effect is particularly problematic for short low-pass signals. Applying the same window to the surrogate data allows having the same nonstationarity. The method is tested on order 1 autoregressive process null hypothesis by Monte Carlo simulations. Previous methods were not able to yield good performances for left- and right-sided tests at the same time, even less with bilateral tests. It is shown that the new method is conservative for unilateral tests and bilateral tests. In order to show that the proposed windowing method can be useful in the real context, in this extended paper, it was applied for an electroencephalogram (EEG) diagnostic problem. A dataset comprising the EEG measurements of 15 subjects distributed in three groups, attention-deficit disorder primarily hyperactive-impulsive (ADHD), attention-deficit disorder primarily inattentive (ADD), and anxiety with attentional fragility (ANX), was used. Both statistical and machine learning (naïve Bayesian) approaches were considered. The mean short-windowed SA (MSWSA) was used as a signal feature, and its performances were studied with respect to the windowing systems. The main findings were that: 1) the MSWSA feature has less variability for ADD than for ADHD or ANX; 2) the proposed windowing method reduces bias and nonnormality of the SA feature; 3) with the proposed method and a naïve Bayesian classifier, a 93% success rate of discriminating ADD from ADHD and ANX was achieved with leave-one-out cross-validation; and 4) the new feature could not have yielded interesting results without the proposed windowing system.
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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 | 0.001 | 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 it