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Updated: Jul 9, 2026

EEG Mu Rhythm in Typical and Atypical Development
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Neurophysiological EEG Characterization of Autism Spectrum Disorder Using DWT-Based Frequency Analysis With Selective

Kazi Mahatir Mohammed Samir1, Adnan Sami Sarker1, Sawrav Das2

  • 1Department of Electrical and Electronic Engineering, Chittagong University of Engineering and Technology, Chittagong, Bangladesh.

FASEB Journal : Official Publication of the Federation of American Societies for Experimental Biology
|July 8, 2026
PubMed
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This summary is machine-generated.

This study introduces an automated engineering framework for Autism Spectrum Disorder (ASD) diagnosis using electroencephalogram (EEG) signals. The system effectively analyzes neural oscillations, identifying key brainwave patterns for objective identification.

Area of Science:

  • Neuroscience
  • Computational Intelligence
  • Signal Processing

Background:

  • Autism Spectrum Disorder (ASD) diagnosis often relies on behavioral observation, necessitating objective biomarkers.
  • Neural oscillations, analyzed via electroencephalogram (EEG), offer a promising avenue for objective ASD identification.

Purpose of the Study:

  • To develop and validate an engineering framework for objective Autism Spectrum Disorder (ASD) diagnosis using EEG-derived neural oscillation analysis.
  • To compare the efficacy of various machine learning and deep learning models for ASD classification based on electrophysiological data.

Main Methods:

  • Utilized multi-channel EEG data from 15 electrodes, organized into seven functional brain regions.
  • Applied Discrete Wavelet Transform (db4) for signal decomposition and extracted spectral power across delta, theta, alpha, beta, and gamma bands.
Keywords:
autism Spectrum disorder (ASD)discrete wavelet transform (DWT)electroencephalography (EEG)explainable artificial intelligence (XAI)machine learningneurophysiological signal characterizationselective electrode and brain segmentation

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  • Engineered a multi-domain feature set by combining spectral characteristics with statistical descriptors for signal dynamics and complexity.

    • Main Results:

    • Logistic Regression achieved the highest accuracy (88.57%) with an AUC of 0.77, followed by Extra Trees (86.42%).
    • Long Short-Term Memory (LSTM) networks demonstrated strong performance with approximately 87% accuracy, highlighting their sequential modeling capabilities.
    • Explainability analysis identified delta and theta band power as the most critical discriminative features for ASD.

    Conclusions:

    • The developed framework provides a technically rigorous, transparent, and automated pipeline for electrophysiological signal characterization in ASD.
    • This study establishes a preliminary computational foundation for the neurophysiological differentiation of pediatric ASD.
    • Findings are preliminary and require validation in larger, independent cohorts for clinical applicability.