A Machine Learning Pipeline for Evaluating Pre-Stimulus EEG Features and Their Impact on Post-Stimulus TMS-EEG Responses
Bibliographic record
Abstract
Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) offers a direct window into cortical excitability and connectivity. Among several TMS-evoked potential (TEP) metrics, the absolute area under the curve (AUC) reflects the total energy of the post-stimulus response. However, the extent to which pre-stimulus neural features influence these responses remains unclear. This study presents a transparent machine learning pipeline to systematically evaluate how pre-stimulus spectral power and complexity measures predict post-stimulus TEPs. Methodological choices-including scaling techniques and feature selection methods (mutual information, LASSO, f-regression)-were rigorously compared and validated. The results demonstrate that gamma band power and Higuchi Fractal Dimension (HFD) consistently yield the highest predictive relevance. The best performance was achieved using the full feature set, suggesting a complementary role of spectral and nonlinear features. These findings emphasize the importance of methodological transparency and robust validation in TMS-EEG analysis. Future work may benefit from incorporating subject-specific modeling and connectivity-based features to enhance both prediction and neuroscientific insight.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
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".