Pole-Zero REM Modeling with Application in EEG Artifact Removal
Bibliographic record
Abstract
A new approach of pole-zero modeling in the presence of white noise is proposed. While the model estimate is calculated through the conventional least square estimation, the choice of number of poles and zeros in this scenario is critical and a challenging task. A wrong choice can overfit the additive noise in larger orders or underfit and discard parts of the noiseless data in smaller orders. To overcome this issue, we choose the order through RE Minimization (REM). RE is the error between the observed noisy data and the unavailable noiseless output. Using the available output error, the method provides a probabilistic worst case upperbound for RE and optimizes it. Simulation results on generated synthetic data show advantages of REM compared to existing order selection methods such as AIC and BIC. The results show that the proposed method avoids over or under parametrizing of AIC and BIC. The results in a practical application of EOG artifacts removal of eye blinks from EEG data provides an efficient modeling of the true background EEG with optimal eye blink removal.
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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 | 0.000 |
| 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".