A Comparison of Adaptive and Non-Adaptive EEG Source Localization Algorithms Using a Realistic Head Model
Bibliographic record
Abstract
An accurate and robust electroencephalogram (EEG) source localization algorithm would be a definite asset for the surgical treatment of patients with epilepsy. Due to the underdetermined nature of the EEG inverse problem, a variety of algorithms with unique constraints and assumptions are applied to select the current dipole source distribution that best accounts for the scalp recordings. We investigated four algorithms: two non-adaptive algorithms: the minimum norm and LORETA as well as two adaptive algorithms: the Borgiotti-Kaplan and eigenspace projection beamformers. Compared over a range of SNR values and single source locations, we found that the eigenspace projection beamformer exhibited superior localizing capabilities compared to the other three algorithms while minimizing source current dispersion. The size of the data window required to accurately localize using the adaptive beamformers was also investigated to improve algorithm efficiency and minimize stationary source assumptions
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".