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Autism Spectrum Disorder

Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.

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Related Experiment Video

Updated: Dec 3, 2025

EEG Mu Rhythm in Typical and Atypical Development

EEG Mu Rhythm in Typical and Atypical Development

Published on: April 9, 2014

A study on EEG feature extraction and classification in autistic children based on singular spectrum analysis method.

Jie Zhao^1,2, Jiajia Song^1,2, Xiaoli Li³

¹Institute of Electronic Information Engineering, Hebei University, Baoding, China.

Brain and Behavior

|October 30, 2020

Summary

Objective biomarkers for Autism Spectrum Disorder (ASD) were identified using electroencephalography (EEG). This method achieved 92.7% accuracy in distinguishing children with ASD from typically developing peers.

Keywords:

alpha peak frequency (APF)autism classification electroencephalography singular spectrum analysis (SSA)

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Area of Science:

Neuroscience
Biomarkers
Clinical Diagnostics

Background:

Current Autism Spectrum Disorder (ASD) diagnosis relies on subjective rating scales.
Objective diagnostic indicators for ASD are critically needed in clinical practice.
Resting-state electroencephalography (EEG) offers a potential source for objective biomarkers.

Purpose of the Study:

To identify objective biomarkers from resting-state EEG data for distinguishing children with ASD from typically developing (TD) children.
To evaluate the diagnostic potential of EEG-derived features in early childhood.

Main Methods:

Resting-state EEG data were collected from 46 children with ASD and 63 age-matched TD children (ages 3-5).
Singular Spectrum Analysis (SSA) was employed to denoise EEG signals and extract the alpha rhythm.
Individualized alpha peak frequency (iAPF) and individualized alpha absolute power (iABP) were extracted as key features.

Main Results:

A linear support vector machine (SVM) classifier was trained using iAPF and iABP.
The classification model achieved a high accuracy of 92.7% in differentiating ASD from TD children.
These findings highlight the efficacy of specific EEG spectral features in ASD detection.

Conclusions:

The developed EEG-based method shows significant potential for assisting in the objective clinical diagnosis of ASD.
Objective biomarkers derived from resting-state EEG could improve diagnostic accuracy and reduce subjectivity.
Further validation is warranted to integrate this approach into routine clinical settings.