TFSNet: A Time–Frequency Synergy Network Based on EEG Signals for Autism Spectrum Disorder Classification

TFSNet: A Time–Frequency Synergy Network Based on EEG Signals for Autism Spectrum Disorder Classification

Autism Spectrum Disorder (ASD) seriously affects social, communication, and behavioral functions, and early accurate diagnosis is crucial to improve the prognosis of patients. Traditional diagnosis methods rely on professional doctors to make subjective diagnosis through scales, the feature extracti...

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Bibliographic Details
Main Authors: Lijuan Shi, Lintao Ma, Jian Zhao, Zhejun Kuang, Sifan Wang, Han Yang, Haiyan Wang, Qiulei Han, Lei Sun
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Brain Sciences
Subjects:
Autism Spectrum Disorder
EEG
time-frequency feature fusion
feature extraction
Online Access:https://www.mdpi.com/2076-3425/15/7/684
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Summary:Autism Spectrum Disorder (ASD) seriously affects social, communication, and behavioral functions, and early accurate diagnosis is crucial to improve the prognosis of patients. Traditional diagnosis methods rely on professional doctors to make subjective diagnosis through scales, the feature extraction of existing machine learning methods is inefficient, and existing deep learning methods have limitations in capturing time-varying features and the joint expression of time–frequency features. To this end, this study proposes a time–frequency synergy network (TFSNet) to improve the accuracy of ASD EEG signal classification. The proposed Dynamic Residual Block (TDRB) was used to enhance time-domain feature extraction; Short-Time Fourier Transform (STFT), convolutional attention mechanism, and transformation technology were combined to capture frequency-domain information; and an adaptive cross-domain attention mechanism (ACDA) was designed to realize efficient fusion of time–frequency features. The experimental results show that the average accuracy of TFSNet on the University of Sheffield (containing 28 ASD patients and 28 healthy controls) and KAU dataset (containing 12 ASD patients and five healthy controls) reaches 98.68%and 97.14%, respectively, yielding significantly better results than the existing machine learning and deep learning methods. In addition, the analysis of model decisions through interpretability analysis techniques enhances its transparency and reliability.
ISSN:2076-3425

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