Fig. 1 | Scientific Reports

Fig. 1

From: Critical role of EEG signals in assessment of sex-specific insights in neurological diagnostics via machine learning approach

Fig. 1

Biological sex classification performance across three populations using EEG signals: (A) Normal, (B) Abnormal, and (C) All participants. Bars represent balanced accuracy (BAcc), and error bars indicate the standard error across ten random seeds. The TUEG dataset does not include pathology labels; therefore, results are only available for the combined population (C). Across datasets, BAcc ranged from 65% to 80%, with slightly higher performance observed in the Normal population compared to the Abnormal population. A notable drop in accuracy is observed when models are tested out-of-distribution, reflecting the challenge of generalizing across heterogeneous EEG datasets.

Back to article page