Deep neurocomputational fusion for ASD diagnosis using multi-domain EEG analysis

Autism spectrum disorder (ASD) presents significant challenges in early detection due to its heterogeneous nature and the subtlety of neurophysiological variations. Electroencephalography (EEG) has emerged as a promising tool for ASD diagnosis, offering insights into intricate neurophysiological sig...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) Vol. 641; p. 130353
Main Authors: Rasool, Abdur, Aslam, Saba, Xu, Yongjie, Wang, Yishan, Pan, Yi, Chen, Weiyang
Format: Journal Article
Language:English
Published: Elsevier B.V 07.08.2025
Subjects:
Autism spectrum disorder
Deep learning
EEG analysis
Fusion model
Neurocomputational diagnostics
Deep learning
Fusion model
Neurocomputational diagnostics
Autism spectrum disorder
EEG analysis
ISSN:0925-2312
Online Access:Get full text
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Summary:Autism spectrum disorder (ASD) presents significant challenges in early detection due to its heterogeneous nature and the subtlety of neurophysiological variations. Electroencephalography (EEG) has emerged as a promising tool for ASD diagnosis, offering insights into intricate neurophysiological signal dynamics. However, existing computational EEG analysis techniques often struggle to effectively capture complex ASD-related neural activity patterns. To address these limitations, this study proposes an Encoder-Ensemble Fusion Model (EEFM), designed to enhance EEG-based ASD classification through advanced neurocomputational modeling. EEFM integrates multi-domain EEG features with a sophisticated fusion network, initially extracting a diverse range of time and frequency-domain characteristics. This model enables the identification of nuanced and heterogeneous ASD-specific neural activity, leveraging ensemble learning and autoencoder architectures to uncover complex relationships within the data. Specifically, an LSTM (Long Short-Term Memory)-autoencoder models temporal and spatial dependencies, followed by an XGBoost Regressor for nonlinear EEG signal interpretation and a linear regression model for decision boundary refinement. The proposed model has been evaluated on a dataset focusing on sex-specific EEG variations in ASD and Typically Developing (TD) subjects. By incorporating EEG-based neurophysiological biomarkers, EEFM achieved 93% accuracy in distinguishing ASD from TD individuals. This model enhances sensitivity to subtle ASD-related abnormalities while addressing challenges in data heterogeneity, scarcity, and generalizability, contributing to advancements in neurocomputing methodologies for EEG-based ASD diagnosis.
ISSN:0925-2312
DOI:10.1016/j.neucom.2025.130353