Efficient Blinking Component Estimation in Subspace-Based EEG and MEG Analysis
Bibliographic record
Abstract
Removing EyeBlink (EB) artifact is often an essential preprocessing step of EEG/MEG analysis. Subspace-based methods such as Independent Component Analysis (ICA) and Signal Source Projection (SSP) are well-known methods of EB artifact removal. However, these methods require preknowledge of the number of components (NoC) involved in the blinking segments which is unknown and estimated by visual inspection. Here, we provide a reliable and efficient method for estimating the NoC of EB. The method utilizes an SVD denoising approach denoted by Number of Source Eigenvalue Error (NoSEE). The proposed method performance is examined by synthetic EEG, and is validated by real MEG data. Root Mean Square Error (RMSE) is used to evaluate the performance while visual inspection on real MEG data is used to validate the algorithms results. In both cases, the proposed method provides optimum NoC in the sense of MSE minimization and consistency with the real data. The method provides more accurate results compared to manual methods. In addition, the low computational complexity of the algorithm results in much less processing time for this automatic NoC calculator compared to the trial and error procedure of the manual methods which enables the algorithm to be used in online EEG analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".