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Predicting Sex From EEG: Validity and Generalizability of Deep-Learning-Based Interpretable Classifier.

Barbora Bučková1,2, Martin Brunovský3,4, Martin Bareš3,4

  • 1Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czechia.

Frontiers in Neuroscience
|November 16, 2020
PubMed
Summary

This study found that beta-band power in electroencephalogram (EEG) recordings can distinguish biological sex in Major Depressive Disorder patients with 77% accuracy. This sex difference in brain activity remained consistent even after antidepressant treatment.

Keywords:
EEGbiomarkersclassificationexplainable artificial intelligencemachine learningmajor depressive disordersexual dimorsphism

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Psychiatry

Background:

  • Explainable AI is crucial for hypothesis generation in neuroscience.
  • A prior study identified biological sex from EEG using a deep convolutional neural network, noting beta-band power's discriminative role.
  • This finding has theoretical implications for neurobiological sexual dimorphisms and practical relevance for quantitative EEG diagnostics.

Purpose of the Study:

  • To test if automatic biological sex identification from EEG generalizes to a new dataset, specifically in patients with Major Depressive Disorder.
  • To investigate the hypothesis of higher beta-band power in women compared to men within this patient cohort.
  • To evaluate the impact of antidepressant treatment on sex classification accuracy.

Main Methods:

  • Utilized EEG recordings from 134 patients with Major Depressive Disorder.
  • Applied machine learning models to automatically identify biological sex based on EEG data.
  • Constructed Receiver Operating Characteristic (ROC) curves to compare classifier performance before and after antidepressant treatment.

Main Results:

  • Replicated a significant difference in beta-band power between men and women, achieving nearly 77% classification accuracy for sex identification.
  • Observed consistency in the beta-band power difference across most electrodes.
  • Found that multivariate classification models did not substantially improve performance.
  • Maintained classification accuracy above 70% for sex identification even after antidepressant treatment.

Conclusions:

  • The significant difference in beta-band power between sexes is robust and observable in Major Depressive Disorder patients.
  • Automatic sex identification from EEG is feasible in this clinical population.
  • The observed sex difference in beta-band power is resilient to antidepressant treatment, suggesting a stable neurobiological dimorphism.