Critical role of EEG signals in assessment of sex-specific insights in neurological diagnostics via machine learning approach
Bibliographic record
Abstract
Early detection and diagnosis of neurological pathology are essential for timely treatment and intervention. While deep learning has shown promise in analyzing brain imaging data, the influence of sex-specific patterns in electroencephalogram (EEG) signals remains underexplored. In this study, we investigated the detectability and impact of biological sex in EEG data using Artificial Intelligence (AI) methods, with a focus on both biological sex classification and its confounding effects in pathological EEG diagnosis. We employed a lightweight yet effective convolutional neural network and evaluated its performance across three diverse EEG datasets (TUEG, TUAB, and NMT), including both healthy and pathological subjects. Our evaluation leveraged datasets from various sources and participant groups, featuring distribution shifts. Our model achieved balanced accuracy ranging from [Formula: see text] to [Formula: see text] in detecting biological sex from EEG signals, demonstrating the robustness and cross-dataset transferability of sex-related neural patterns. While the AI models demonstrated accurate biological sex detection on datasets without fine-tuning, their performance declined with significant distribution shifts. Furthermore, we explored the relationship between biological sex and pathology by visualizing salient features for target detection across distinct subgroups. Our findings revealed unprecedented insights into the negligible role of sex-specific patterns in pathology detection despite the presence of prominent and consistent patterns within each biological sex group. These findings are critical for advancing the development of more robust and unbiased AI models in disease prediction, as well as for informing treatment paradigms.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".