| Résumé | 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 to 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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