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arXiv · 2012.11105

Resting-state EEG sex classification using selected brain connectivity representation

Jean Li·Jeremiah D. Deng·Divya Adhia·Dirk de Ridder
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Abstract

Effective analysis of EEG signals for potential clinical applications remains a challenging task. So far, the analysis and conditioning of EEG have largely remained sex-neutral. This paper employs a machine learning approach to explore the evidence of sex effects on EEG signals, and confirms the generality of these effects by achieving successful sex prediction of resting-state EEG signals. We have found that the brain connectivity represented by the coherence between certain sensor channels are good predictors of sex.

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Jean Li, Jeremiah D. Deng, Divya Adhia, Dirk de Ridder. 2020-12-21. Resting-state EEG sex classification using selected brain connectivity representation. https://arxiv.org/abs/2012.11105

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