1. Predicting Sex From EEG: Validity and Generalizability of Deep-Learning-Based Interpretable Classifier
- Author
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Barbora Bučková, Martin Brunovský, Martin Bareš, and Jaroslav Hlinka
- Subjects
0301 basic medicine ,sexual dimorsphism ,Electroencephalography ,Convolutional neural network ,lcsh:RC321-571 ,03 medical and health sciences ,0302 clinical medicine ,Discriminative model ,medicine ,Generalizability theory ,EEG ,lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry ,explainable artificial intelligence ,major depressive disorder ,Receiver operating characteristic ,medicine.diagnostic_test ,business.industry ,Deep learning ,General Neuroscience ,biomarkers ,Replicate ,Brief Research Report ,medicine.disease ,machine learning ,030104 developmental biology ,classification ,Major depressive disorder ,Artificial intelligence ,business ,Psychology ,030217 neurology & neurosurgery ,Neuroscience ,Cognitive psychology - Abstract
Explainable artificial intelligence holds a great promise for neuroscience and plays an important role in the hypothesis generation process. We follow-up a recent machine learning-oriented study that constructed a deep convolutional neural network to automatically identify biological sex from EEG recordings in healthy individuals and highlighted the discriminative role of beta-band power. If generalizing, this finding would be relevant not only theoretically by pointing to some specific neurobiological sexual dimorphisms, but potentially also as a relevant confound in quantitative EEG diagnostic practice. To put this finding to test, we assess whether the automatic identification of biological sex generalizes to another dataset, particularly in the presence of a psychiatric disease, by testing the hypothesis of higher beta power in women compared to men on 134 patients suffering from Major Depressive Disorder. Moreover, we construct ROC curves and compare the performance of the classifiers in determining sex both before and after the antidepressant treatment. We replicate the observation of a significant difference in beta-band power between men and women, providing classification accuracy of nearly 77%. The difference was consistent across the majority of electrodes, however multivariate classification models did not generally improve the performance. Similar results were observed also after the antidepressant treatment (classification accuracy above 70%), further supporting the robustness of the initial finding.
- Published
- 2020
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