Automated Pre-Processing and Automated Post-Processing in EEG/MEG Brain Source Analysis
Bibliographic record
Abstract
Analyzing Electroencephalography (EEG)/ Magnetoencephalography (MEG) brain source signals allows for a better understanding and diagnosis of various brain-related activities or injuries. Due to the high complexity of the mentioned measurements and their low spatial resolution, different techniques have been employed to enhance the quality of the obtained results. The objective of this work is to employ state-of-the-art approaches and develop algorithms with higher analysis reliability. As a pre-processing method, subspace denoising and artifact removal approaches are taken into consideration, to provide a method that automates and improves the estimation of the Number of Component (NoC) for artifacts such as Eye Blinking (EB). By using synthetic EEG-like simulation and real MEG data, it is shown that the proposed method is more reliable over the conventional manual method in estimating the NoC. For Independent Component Analysis (ICA)-based approaches, the proposed method in this thesis provides an estimation for the number of components with an accuracy of 98.7%. The thesis is also devoted to improving source localization techniques, which aims to estimate the location of the source within the brain, which elicit time-series measurements. In this context, after obtaining a practical insight into the performance of the popular L2-Regularization based approaches, a post-processing thresholding method is introduced. The proposed method improves the spatial resolution of the L2-Regularization inverse solutions, especially for Standard Low-Resolution Electromagnetic Tomography (sLORETA), which is a well-known and widely used inverse solution. As a part of the proposed method, a novel noise variance estimation is introduced, which combines the kurtosis statistical parameter and data (noise) entropy. This new noise variance estimation technique allows for a superior performance of the proposed method compared to the existing ones. The algorithm is validated on the synthetic EEG data using well-established validation metrics. It is shown that the proposed solution improves the resolution of conventional methods in the process of thresholding/denoising automatically and without loss of any critical information.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".