Dance of the neurons: unraveling sex from brain signals
Bibliographic record
Abstract
Previous studies have shown that machine learning can predict biological sex from EEG data with high accuracy. However, the validity and generalizability of these findings across different datasets and tasks still need to be clarified. In this paper, we investigated the robustness and transferability of sex-related patterns in EEG data using a Convolutional neural network (CNN) trained on several corpora of EEG recordings ranging from 221 to 12, 000 participants from healthy and diseased subjects. We evaluated the CNN on datasets from various sources and groups, with varying degrees of shift in their distributions. We found that CNNs can detect sex from EEG data accurately on datasets without fine-tuning or adaptation when the shift is low. However, performance drops where the shift is drastic. These results suggest that sex-related patterns in EEG data are robust and transferable across diverse datasets and relevant tasks. We discuss the implications of these findings for EEG analysis, machine learning applications, and best practices to avoid sex biases that enhance personalized mental health interventions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".