A wavelet based de-noising technique for ocular artifact correction of the electroencephalogram
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates a wavelet based denoising of the electroencephalogram (EEG) signal to correct for the presence of the ocular artifact (OA). The. proposed technique is based on an over-complete wavelet expansion of the EEG as follows: i) a stationary wavelet transform (SWT) is applied to the corrupted EEG; ii) the thresholding of the coefficients in the lower frequency bands is performed; iii) the de-noised signal is reconstructed. This paper demonstrates the potential of the proposed technique for successful OA correction. The advantage over conventional methods is that there is no need for the recording of the electrooculogram (EOG) signal itself. The approach works both for eye blinks and eye movements. Hence, there is no need to discriminate between different artifacts. To allow for a proper comparison, the contaminated EEG signals as well as the corrected signals are presented together with their corresponding power spectra.
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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 | 0.000 |
| 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 it