Detection and removal of ocular artifacts using Independent Component Analysis and wavelets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper a novel approach for ocular artifact (OA) removal is proposed in which a combination of independent component analysis and wavelet-based noise reduction is utilized for detection and removal of OA. At the first stage, independent basis functions attributed to OA are computed using FastICA algorithm. This is followed by designing a wavelet basis function which is tuned to have sufficient similarity in its waveform to the independent basis functions of OA. We then utilize the designed wavelet for signal decomposition in a standard discrete wavelet transform where by deleting the approximation and summing up the details of signal decomposition, we arrive at a sufficiently artifact-free EEG signal. The approach excludes thresholding challenges of wavelets and works both for eye blinks and eye movements. Applying our algorithm to 420 4-s EEG epochs, the method exhibits high performance for the removal of OA artifacts. Our wavelet design method for noise reduction can be extended to the removal other types of EEG artifacts.
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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