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Related Experiment Video

Updated: Jul 13, 2025

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
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Resting-state EEG Microstate Features Can Quantitatively Predict Autistic Traits in Typically Developing Individuals.

Huibin Jia1,2, Xiangci Wu1,2, Xiaolin Zhang1,2

  • 1Institute of Psychology and Behavior, Henan University, Kaifeng, 475004, China.

Brain Topography
|October 13, 2023
PubMed
Summary

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Autism spectrum disorder (ASD) traits exist on a spectrum. Machine learning analysis of EEG microstate features can predict autistic traits, aiding in objective assessment.

Area of Science:

  • Neuroscience
  • Psychiatry
  • Machine Learning

Background:

  • Autism spectrum disorder (ASD) symptoms vary widely, even in the general population.
  • Individuals with high autistic traits show similar behavioral and neural differences to diagnosed ASD.
  • Objective tools are needed to assess autistic traits.

Purpose of the Study:

  • To develop a machine learning model for assessing autistic traits.
  • To utilize electroencephalography (EEG) microstate features from resting-state recordings.

Main Methods:

  • Applied Least Absolute Shrinkage and Selection Operator (LASSO) and correlation analysis to identify key EEG microstate features.
  • Developed a Support Vector Regression (SVR) model to predict autistic trait scores using selected features.
Keywords:
Autistic traitsEEG microstatesFeature selectionMachine learning

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  • Used resting-state EEG recordings for feature extraction.

    • Main Results:

    • Identified four crucial microstate features: mean duration of class D, occurrence rate of class A, time coverage of class D, and B-to-D transition rate.
    • The SVR model accurately predicted autistic trait scores, showing a good match with self-reported scores.
    • Demonstrated the predictive capability of EEG microstate analysis for autistic traits.

    Conclusions:

    • Resting-state EEG microstate analysis is a viable method for predicting autistic traits.
    • This technique offers a potential objective tool for assessing autistic traits.
    • Further research can refine this approach for clinical applications.