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Michel J A M van Putten1, Sebastian Olbrich2, Martijn Arns3

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Biometrics

Background:

  • Human sex can be visually assessed from faces, but not readily from brain activity.
  • Deep convolutional neural networks excel at identifying subtle patterns in complex data.

Purpose of the Study:

  • To investigate if sex-specific information is present in human brain rhythms.
  • To determine if deep neural networks can predict sex from electroencephalograms (EEG).

Main Methods:

  • Utilized deep convolutional neural networks to analyze scalp electroencephalograms (EEG).
  • Trained a deep neural net on EEG data to predict biological sex.
  • Extracted sex-specific features from the neural network's filter layers.

Main Results:

  • The deep neural network predicted sex from EEG with >80% accuracy (p < 10⁻⁵).
  • Brain rhythms were found to be sex-specific.
  • Fast beta activity (20-25 Hz) and its spatial distribution were identified as key distinguishing features.

Conclusions:

  • Deep neural networks can detect subtle, sex-specific features in spatiotemporal EEG data.
  • This approach facilitates knowledge discovery in neuroscience.
  • The methodology holds potential for applications in neurology, cardiology, and neuropsychology.