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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| Vydané v: | Neurocomputing (Amsterdam) Ročník 641; s. 130353 |
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| Jazyk: | English |
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Elsevier B.V
07.08.2025
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| ISSN: | 0925-2312 |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 130353 |
| Author | Aslam, Saba Rasool, Abdur Pan, Yi Chen, Weiyang Xu, Yongjie Wang, Yishan |
| Author_xml | – sequence: 1 givenname: Abdur orcidid: 0000-0001-5334-9001 surname: Rasool fullname: Rasool, Abdur organization: Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, 96822, HI, USA – sequence: 2 givenname: Saba orcidid: 0000-0001-7681-4324 surname: Aslam fullname: Aslam, Saba organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China – sequence: 3 givenname: Yongjie orcidid: 0000-0002-4270-1855 surname: Xu fullname: Xu, Yongjie organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China – sequence: 4 givenname: Yishan orcidid: 0000-0002-6674-8180 surname: Wang fullname: Wang, Yishan email: ys.wang@siat.ac.cn organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China – sequence: 5 givenname: Yi orcidid: 0000-0002-2766-3096 surname: Pan fullname: Pan, Yi email: yi.pan@siat.ac.cn organization: Shenzhen Key Laboratory of Intelligent Bioinformatics, Shenzhen Institutes of Advanced Technology, Shenzhen, 518055, Guangdong, China – sequence: 6 givenname: Weiyang orcidid: 0000-0001-8430-7381 surname: Chen fullname: Chen, Weiyang organization: School of Cyber Science and Engineering, Qufu Normal University, Qufu, 273165, Shandong, China |
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