Sex-related patterns in the electroencephalogram and their relevance in machine learning classifiers.

ABSTRACT: Deep learning is increasingly being proposed for detecting neurological and psychiatric diseases from electroencephalogram (EEG) data but the method is prone to inadvertently incorporate biases from training data and exploit illegitimate patterns. The recent demonstration that deep learning can detect the sex from EEG implies potential sex-related biases in deep learning-based disease detectors for the many diseases with unequal prevalence between males and females. In this work, we present the male- and female-typical patterns used by a convolutional neural network that detects the sex from clinical EEG (81% accuracy in a separate test set with 142 patients). We considered neural sources, anatomical differences, and non-neural artifacts as sources of differences in the EEG curves. Using E

SUBMITTER: Jochmann T 

PROVIDER: S-EPMC10472918 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature