Automated EEG Source Error Thresholding (AESET) in L<sub>2</sub>-Regularization Inverse Problems
Bibliographic record
Abstract
Brain source localization techniques aim to provide high spatial resolution for EEG data which are known to have a high temporal resolution. Yet, this task is very challenging and limited due to the influence of volume conduction effect. Most popular inverse solutions which provide a reasonable estimate for the location of the source while being simple and computationally fast, such as the L2-Regularization based category of solutions, tends to provide low resolution and blurred estimated. Here we present a probabilistic approach for automating the thresholding of the L2-based inverse solutions that will result in a more reliable solution. The approach utilizes Minimum Noiseless Description Length (MNDL) thresholding. Addition of this method to the result of the inverse solutions confines the need for manual thresholding. The method`s performance is evaluated in a series of synthetic simulations with the change in parameters such as Signal-to-Noise Ratio (SNR) and the number of source patches. Mean Square Error (MSE) and Percentage of Undetermined Source (PUS) are used to evaluate the performance of the thresholding method, and the results illustrate the advantage of this automatic thresholding over the manual thresholding.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".