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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| Vydáno v: | Neurocomputing (Amsterdam) Ročník 641; s. 130353 |
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| Jazyk: | angličtina |
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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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| Cites_doi | 10.1016/j.bbe.2020.02.002 10.1016/j.conb.2018.06.013 10.1177/1550059420982424 10.1109/RBME.2020.2969915 10.1016/j.rasd.2020.101671 10.1016/j.neucom.2024.129315 10.1016/j.cmpb.2024.108492 10.1515/revneuro-2020-0043 10.1109/TNSRE.2022.3188564 10.3389/fninf.2019.00070 10.3390/ijms21134726 10.1007/s00500-021-05877-0 10.1155/2020/6925107 10.1016/j.neucom.2024.128644 10.1016/j.neucom.2020.05.126 10.1080/03036758.2020.1780274 10.1007/s00521-022-07822-0 10.1016/j.neucom.2023.126262 10.1097/WNP.0000000000000510 10.1016/j.biopsych.2020.02.015 10.1016/j.neucom.2021.04.009 10.1088/1741-2552/aab2f2 10.3390/app7121239 10.1016/j.neuroimage.2022.119774 10.1016/j.neucom.2024.129203 10.1016/j.compbiomed.2024.109558 10.1016/j.cmpb.2024.108196 10.1016/j.eswa.2023.120391 10.3389/fninf.2019.00032 10.1016/j.compbiomed.2021.104548 10.1016/j.cogr.2021.04.001 10.1088/1741-2552/ad8e86 10.3390/app10217453 |
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| References | Ul Amin, Kim, Jung, Seo, Park (b38) 2024; 6 Al-Nafjan, Hosny, Al-Ohali, Al-Wabil (b25) 2017; 7 Genovese, Butler (b13) 2020; 21 Mao, Fathurrahman, Lee, Chang (b26) 2020; 1456 Grossi, Valbusa, Buscema (b36) 2021; 52 Dong, Chen, Zhang, Ke, Li (b30) 2021; 449 Aboyeji, Ahmad, Wang, Chen, Yao, Li, Tong, Siu, Zhao, Chen (b3) 2025; 185 Wadhera, Kakkar, Rani (b22) 2021 Eslami, Mirjalili, Fong, Laird, Saeed (b34) 2019; 13 Nogay, Adeli (b14) 2020; 31 Tan, Zhang, Wang, Chen, Li, Gao, Zeng (b40) 2025; 622 Xu, Yan, Deng, Yang, Liu, Wang, Yuan (b16) 2024; 610 Wang, Wang (b19) 2021; 1 Mansvelder, Verhoog, Goriounova (b12) 2019; 54 Lotte, Bougrain, Cichocki, Clerc, Congedo, Rakotomamonjy, Yger (b23) 2018; 15 Ives-Deliperi, Butler (b7) 2018; 35 Adhikari, Shrestha, Mishra, Singh, Timalsina (b29) 2018 Baygin, Dogan, Tuncer, Barua, Faust, Arunkumar, Abdulhay, Palmer, Acharya (b37) 2021; 134 Xu, Yu, Li, Liu, Li, Wang (b28) 2024; 250 Niso, Romero, Moreau, Araujo, Krol (b8) 2023; 269 Chen, Lu, Xie, Shang (b9) 2020; 2020 Bunterngchit, Wang, Su, Wang, Liu, Hou (b5) 2024; 21 Guimarães, Araujo, Araujo, Batista, de Campos Souza (b27) 2019 Vidivelli, Padmakumari, Shanthi (b42) 2025; 260 Zhang, Zhang, Chen, Jiang, Li, Dong, Feng, Yao, Li, Xu (b6) 2022; 30 Amin, Hussain, Kim, Seo (b41) 2023; 228 Torres, Clarkson, Hauschild, Luhmann, Lerner, Riccardi (b31) 2022; 7 Albahri, Hamid, Albahri (b18) 2023; 35 Soufineyestani, Dowling, Khan (b4) 2020; 10 McCarty, Frye (b17) 2020; 35 Rukhsar, Tiwari (b20) 2025; 620 Khosla, Chand (b21) 2020; 40 Kirk, Sumner (b11) 2021; 51 Vidivelli, Padmakumari, Shanthi (b15) 2025; 260 Pender, Fearon, Heron, Mandy (b33) 2020; 79 Zhou, Li, Li, Ji, Shi, Zheng, Zhang, Chen, Cheng (b2) 2023; 544 Hosseini, Hosseini, Ahi (b24) 2020; 14 Fan, Markram (b1) 2019; 13 Lombardi, Shriki, de Arcangelis (b10) 2021; 461 Feczko, Fair (b32) 2020; 88 Souza, Guimaraes, Araujo, Lughofer (b35) 2021; 25 Saba, Abdur, Hongyan, Xiaoli (b39) 2024 Pender (10.1016/j.neucom.2025.130353_b33) 2020; 79 Nogay (10.1016/j.neucom.2025.130353_b14) 2020; 31 Wang (10.1016/j.neucom.2025.130353_b19) 2021; 1 Khosla (10.1016/j.neucom.2025.130353_b21) 2020; 40 Xu (10.1016/j.neucom.2025.130353_b16) 2024; 610 Bunterngchit (10.1016/j.neucom.2025.130353_b5) 2024; 21 Chen (10.1016/j.neucom.2025.130353_b9) 2020; 2020 Kirk (10.1016/j.neucom.2025.130353_b11) 2021; 51 Mansvelder (10.1016/j.neucom.2025.130353_b12) 2019; 54 Feczko (10.1016/j.neucom.2025.130353_b32) 2020; 88 Zhou (10.1016/j.neucom.2025.130353_b2) 2023; 544 Zhang (10.1016/j.neucom.2025.130353_b6) 2022; 30 Niso (10.1016/j.neucom.2025.130353_b8) 2023; 269 Grossi (10.1016/j.neucom.2025.130353_b36) 2021; 52 Ul Amin (10.1016/j.neucom.2025.130353_b38) 2024; 6 Xu (10.1016/j.neucom.2025.130353_b28) 2024; 250 Soufineyestani (10.1016/j.neucom.2025.130353_b4) 2020; 10 Rukhsar (10.1016/j.neucom.2025.130353_b20) 2025; 620 Baygin (10.1016/j.neucom.2025.130353_b37) 2021; 134 Lombardi (10.1016/j.neucom.2025.130353_b10) 2021; 461 Aboyeji (10.1016/j.neucom.2025.130353_b3) 2025; 185 Vidivelli (10.1016/j.neucom.2025.130353_b42) 2025; 260 Lotte (10.1016/j.neucom.2025.130353_b23) 2018; 15 Albahri (10.1016/j.neucom.2025.130353_b18) 2023; 35 Hosseini (10.1016/j.neucom.2025.130353_b24) 2020; 14 Ives-Deliperi (10.1016/j.neucom.2025.130353_b7) 2018; 35 Eslami (10.1016/j.neucom.2025.130353_b34) 2019; 13 Genovese (10.1016/j.neucom.2025.130353_b13) 2020; 21 Wadhera (10.1016/j.neucom.2025.130353_b22) 2021 Al-Nafjan (10.1016/j.neucom.2025.130353_b25) 2017; 7 Adhikari (10.1016/j.neucom.2025.130353_b29) 2018 Dong (10.1016/j.neucom.2025.130353_b30) 2021; 449 Saba (10.1016/j.neucom.2025.130353_b39) 2024 Tan (10.1016/j.neucom.2025.130353_b40) 2025; 622 Amin (10.1016/j.neucom.2025.130353_b41) 2023; 228 Mao (10.1016/j.neucom.2025.130353_b26) 2020; 1456 McCarty (10.1016/j.neucom.2025.130353_b17) 2020; 35 Souza (10.1016/j.neucom.2025.130353_b35) 2021; 25 Torres (10.1016/j.neucom.2025.130353_b31) 2022; 7 Vidivelli (10.1016/j.neucom.2025.130353_b15) 2025; 260 Guimarães (10.1016/j.neucom.2025.130353_b27) 2019 Fan (10.1016/j.neucom.2025.130353_b1) 2019; 13 |
| References_xml | – volume: 449 start-page: 136 year: 2021 end-page: 145 ident: b30 article-title: Subject sensitive EEG discrimination with fast reconstructable CNN driven by reinforcement learning: A case study of ASD evaluation publication-title: Neurocomputing – volume: 620 year: 2025 ident: b20 article-title: ARNN: Attentive recurrent neural network for multi-channel EEG signals to identify epileptic seizures publication-title: Neurocomputing – volume: 79 year: 2020 ident: b33 article-title: The longitudinal heterogeneity of autistic traits: A systematic review publication-title: Res. Autism Spectr. Disord. – volume: 35 start-page: 504 year: 2018 end-page: 509 ident: b7 article-title: Relationship between EEG electrode and functional cortex in the international 10 to 20 system publication-title: J. Clin. Neurophysiol. – volume: 610 year: 2024 ident: b16 article-title: EEG-based epileptic seizure detection using deep learning techniques: A survey publication-title: Neurocomputing – volume: 6 year: 2024 ident: b38 article-title: Video anomaly detection utilizing efficient spatiotemporal feature fusion with 3D convolutions and long short-term memory modules publication-title: Adv. Intell. Syst. – volume: 21 start-page: 4726 year: 2020 ident: b13 article-title: Clinical assessment, genetics, and treatment approaches in autism spectrum disorder (ASD) publication-title: Int. J. Mol. Sci. – start-page: 279 year: 2021 end-page: 298 ident: b22 article-title: Behavioral modeling using deep neural network framework for ASD diagnosis and prognosis publication-title: Emerg. Technol. Heal.: Internet Things Deep. Learn. Model. – volume: 228 year: 2023 ident: b41 article-title: Deep learning based active learning technique for data annotation and improve the overall performance of classification models publication-title: Expert Syst. Appl. – volume: 13 start-page: 70 year: 2019 ident: b34 article-title: ASD-DiagNet: a hybrid learning approach for detection of autism spectrum disorder using fMRI data publication-title: Front. Neuroinform. – volume: 1456 year: 2020 ident: b26 article-title: EEG dataset classification using CNN method publication-title: J. Phys.: Conf. Ser. – year: 2024 ident: b39 article-title: CEL: A continual learning model for disease outbreak prediction by leveraging domain adaptation via elastic weight consolidation – volume: 250 year: 2024 ident: b28 article-title: Autism spectrum disorder diagnosis with EEG signals using time series maps of brain functional connectivity and a combined CNN–LSTM model publication-title: Comput. Methods Programs Biomed. – volume: 622 year: 2025 ident: b40 article-title: SEDA-EEG: A semi-supervised emotion recognition network with domain adaptation for cross-subject EEG analysis publication-title: Neurocomputing – volume: 25 start-page: 9163 year: 2021 end-page: 9183 ident: b35 article-title: An intelligent Bayesian hybrid approach to help autism diagnosis publication-title: Soft Comput. – volume: 7 start-page: 1239 year: 2017 ident: b25 article-title: Review and classification of emotion recognition based on EEG brain-computer interface system research: a systematic review publication-title: Appl. Sci. – volume: 544 year: 2023 ident: b2 article-title: Progressive graph convolution network for EEG emotion recognition publication-title: Neurocomputing – volume: 30 start-page: 1898 year: 2022 end-page: 1907 ident: b6 article-title: Predicting the symptom severity in autism spectrum disorder based on EEG metrics publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – volume: 134 year: 2021 ident: b37 article-title: Automated ASD detection using hybrid deep lightweight features extracted from EEG signals publication-title: Comput. Biol. Med. – volume: 7 start-page: 688 year: 2022 end-page: 695 ident: b31 article-title: Facial emotions are accurately encoded in the neural signal of those with autism spectrum disorder: A deep learning approach publication-title: Biol. Psychiatry: Cogn. Neurosci. Neuroimaging – volume: 2020 year: 2020 ident: b9 article-title: Anomaly detection in EEG signals: a case study on similarity measure publication-title: Comput. Intell. Neurosci. – volume: 54 start-page: 186 year: 2019 end-page: 193 ident: b12 article-title: Synaptic plasticity in human cortical circuits: cellular mechanisms of learning and memory in the human brain? publication-title: Curr. Opin. Neurobiol. – volume: 35 year: 2020 ident: b17 article-title: Early detection and diagnosis of autism spectrum disorder: Why is it so difficult? publication-title: Seminars in Pediatric Neurology – volume: 31 start-page: 825 year: 2020 end-page: 841 ident: b14 article-title: Machine learning (ML) for the diagnosis of autism spectrum disorder (ASD) using brain imaging publication-title: Rev. Neurosci. – volume: 15 year: 2018 ident: b23 article-title: A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update publication-title: J. Neural Eng. – volume: 35 start-page: 921 year: 2023 end-page: 947 ident: b18 article-title: Early automated prediction model for the diagnosis and detection of children with autism spectrum disorders based on effective sociodemographic and family characteristic features publication-title: Neural Comput. Appl. – start-page: 218 year: 2018 end-page: 223 ident: b29 article-title: EEG based directional signal classification using RNN variants publication-title: 2018 IEEE 3rd International Conference on Computing, Communication and Security – volume: 185 year: 2025 ident: b3 article-title: DCSENets: Interpretable deep learning for patient-independent seizure classification using enhanced EEG-based spectrogram visualization publication-title: Comput. Biol. Med. – volume: 21 year: 2024 ident: b5 article-title: Temporal attention fusion network with custom loss function for EEG–fNIRS classification publication-title: J. Neural Eng. – volume: 260 year: 2025 ident: b42 article-title: Multimodal autism detection: Deep hybrid model with improved feature level fusion publication-title: Comput. Methods Programs Biomed. – volume: 1 start-page: 29 year: 2021 end-page: 40 ident: b19 article-title: Review of the emotional feature extraction and classification using EEG signals publication-title: Cogn. Robot. – volume: 40 start-page: 649 year: 2020 end-page: 690 ident: b21 article-title: A comparative analysis of signal processing and classification methods for different applications based on EEG signals publication-title: Biocybern. Biomed. Eng. – volume: 51 start-page: 24 year: 2021 end-page: 40 ident: b11 article-title: Human EEG and the mechanisms of memory: investigating long-term potentiation (LTP) in sensory-evoked potentials publication-title: J. R. Soc. N. Z. – volume: 14 start-page: 204 year: 2020 end-page: 218 ident: b24 article-title: A review on machine learning for EEG signal processing in bioengineering publication-title: IEEE Rev. Biomed. Eng. – start-page: 401 year: 2019 end-page: 412 ident: b27 article-title: A hybrid model based on fuzzy rules to act on the diagnosed of autism in adults publication-title: IFIP International Conference on Artificial Intelligence Applications and Innovations – volume: 461 start-page: 657 year: 2021 end-page: 666 ident: b10 article-title: Long-range temporal correlations in the broadband resting state activity of the human brain revealed by neuronal avalanches publication-title: Neurocomputing – volume: 269 year: 2023 ident: b8 article-title: Wireless EEG: A survey of systems and studies publication-title: NeuroImage – volume: 260 year: 2025 ident: b15 article-title: Multimodal autism detection: Deep hybrid model with improved feature level fusion publication-title: Comput. Methods Programs Biomed. – volume: 88 start-page: 9 year: 2020 end-page: 17 ident: b32 article-title: Methods and challenges for assessing heterogeneity publication-title: Biol. Psychiatry – volume: 52 start-page: 330 year: 2021 end-page: 337 ident: b36 article-title: Detection of an autism EEG signature from only two EEG channels through features extraction and advanced machine learning analysis publication-title: Clin. EEG Neurosci. – volume: 10 start-page: 7453 year: 2020 ident: b4 article-title: Electroencephalography (EEG) technology applications and available devices publication-title: Appl. Sci. – volume: 13 start-page: 32 year: 2019 ident: b1 article-title: A brief history of simulation neuroscience publication-title: Front. Neuroinformatics – volume: 40 start-page: 649 issue: 2 year: 2020 ident: 10.1016/j.neucom.2025.130353_b21 article-title: A comparative analysis of signal processing and classification methods for different applications based on EEG signals publication-title: Biocybern. Biomed. Eng. doi: 10.1016/j.bbe.2020.02.002 – volume: 54 start-page: 186 year: 2019 ident: 10.1016/j.neucom.2025.130353_b12 article-title: Synaptic plasticity in human cortical circuits: cellular mechanisms of learning and memory in the human brain? publication-title: Curr. Opin. Neurobiol. doi: 10.1016/j.conb.2018.06.013 – volume: 52 start-page: 330 issue: 5 year: 2021 ident: 10.1016/j.neucom.2025.130353_b36 article-title: Detection of an autism EEG signature from only two EEG channels through features extraction and advanced machine learning analysis publication-title: Clin. EEG Neurosci. doi: 10.1177/1550059420982424 – volume: 14 start-page: 204 year: 2020 ident: 10.1016/j.neucom.2025.130353_b24 article-title: A review on machine learning for EEG signal processing in bioengineering publication-title: IEEE Rev. Biomed. Eng. doi: 10.1109/RBME.2020.2969915 – start-page: 279 year: 2021 ident: 10.1016/j.neucom.2025.130353_b22 article-title: Behavioral modeling using deep neural network framework for ASD diagnosis and prognosis publication-title: Emerg. Technol. Heal.: Internet Things Deep. Learn. Model. – volume: 35 year: 2020 ident: 10.1016/j.neucom.2025.130353_b17 article-title: Early detection and diagnosis of autism spectrum disorder: Why is it so difficult? – volume: 79 year: 2020 ident: 10.1016/j.neucom.2025.130353_b33 article-title: The longitudinal heterogeneity of autistic traits: A systematic review publication-title: Res. Autism Spectr. Disord. doi: 10.1016/j.rasd.2020.101671 – volume: 622 year: 2025 ident: 10.1016/j.neucom.2025.130353_b40 article-title: SEDA-EEG: A semi-supervised emotion recognition network with domain adaptation for cross-subject EEG analysis publication-title: Neurocomputing doi: 10.1016/j.neucom.2024.129315 – volume: 260 year: 2025 ident: 10.1016/j.neucom.2025.130353_b42 article-title: Multimodal autism detection: Deep hybrid model with improved feature level fusion publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2024.108492 – volume: 31 start-page: 825 issue: 8 year: 2020 ident: 10.1016/j.neucom.2025.130353_b14 article-title: Machine learning (ML) for the diagnosis of autism spectrum disorder (ASD) using brain imaging publication-title: Rev. Neurosci. doi: 10.1515/revneuro-2020-0043 – volume: 30 start-page: 1898 year: 2022 ident: 10.1016/j.neucom.2025.130353_b6 article-title: Predicting the symptom severity in autism spectrum disorder based on EEG metrics publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2022.3188564 – volume: 13 start-page: 70 year: 2019 ident: 10.1016/j.neucom.2025.130353_b34 article-title: ASD-DiagNet: a hybrid learning approach for detection of autism spectrum disorder using fMRI data publication-title: Front. Neuroinform. doi: 10.3389/fninf.2019.00070 – volume: 1456 issue: 1 year: 2020 ident: 10.1016/j.neucom.2025.130353_b26 article-title: EEG dataset classification using CNN method publication-title: J. Phys.: Conf. Ser. – volume: 21 start-page: 4726 issue: 13 year: 2020 ident: 10.1016/j.neucom.2025.130353_b13 article-title: Clinical assessment, genetics, and treatment approaches in autism spectrum disorder (ASD) publication-title: Int. J. Mol. Sci. doi: 10.3390/ijms21134726 – volume: 7 start-page: 688 issue: 7 year: 2022 ident: 10.1016/j.neucom.2025.130353_b31 article-title: Facial emotions are accurately encoded in the neural signal of those with autism spectrum disorder: A deep learning approach publication-title: Biol. Psychiatry: Cogn. Neurosci. Neuroimaging – volume: 25 start-page: 9163 issue: 14 year: 2021 ident: 10.1016/j.neucom.2025.130353_b35 article-title: An intelligent Bayesian hybrid approach to help autism diagnosis publication-title: Soft Comput. doi: 10.1007/s00500-021-05877-0 – volume: 2020 year: 2020 ident: 10.1016/j.neucom.2025.130353_b9 article-title: Anomaly detection in EEG signals: a case study on similarity measure publication-title: Comput. Intell. Neurosci. doi: 10.1155/2020/6925107 – volume: 610 year: 2024 ident: 10.1016/j.neucom.2025.130353_b16 article-title: EEG-based epileptic seizure detection using deep learning techniques: A survey publication-title: Neurocomputing doi: 10.1016/j.neucom.2024.128644 – volume: 461 start-page: 657 year: 2021 ident: 10.1016/j.neucom.2025.130353_b10 article-title: Long-range temporal correlations in the broadband resting state activity of the human brain revealed by neuronal avalanches publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.05.126 – volume: 51 start-page: 24 issue: 1 year: 2021 ident: 10.1016/j.neucom.2025.130353_b11 article-title: Human EEG and the mechanisms of memory: investigating long-term potentiation (LTP) in sensory-evoked potentials publication-title: J. R. Soc. N. Z. doi: 10.1080/03036758.2020.1780274 – volume: 35 start-page: 921 issue: 1 year: 2023 ident: 10.1016/j.neucom.2025.130353_b18 article-title: Early automated prediction model for the diagnosis and detection of children with autism spectrum disorders based on effective sociodemographic and family characteristic features publication-title: Neural Comput. Appl. doi: 10.1007/s00521-022-07822-0 – volume: 544 year: 2023 ident: 10.1016/j.neucom.2025.130353_b2 article-title: Progressive graph convolution network for EEG emotion recognition publication-title: Neurocomputing doi: 10.1016/j.neucom.2023.126262 – volume: 6 issue: 1 year: 2024 ident: 10.1016/j.neucom.2025.130353_b38 article-title: Video anomaly detection utilizing efficient spatiotemporal feature fusion with 3D convolutions and long short-term memory modules publication-title: Adv. Intell. Syst. – volume: 35 start-page: 504 issue: 6 year: 2018 ident: 10.1016/j.neucom.2025.130353_b7 article-title: Relationship between EEG electrode and functional cortex in the international 10 to 20 system publication-title: J. Clin. Neurophysiol. doi: 10.1097/WNP.0000000000000510 – year: 2024 ident: 10.1016/j.neucom.2025.130353_b39 – volume: 88 start-page: 9 issue: 1 year: 2020 ident: 10.1016/j.neucom.2025.130353_b32 article-title: Methods and challenges for assessing heterogeneity publication-title: Biol. Psychiatry doi: 10.1016/j.biopsych.2020.02.015 – volume: 449 start-page: 136 year: 2021 ident: 10.1016/j.neucom.2025.130353_b30 article-title: Subject sensitive EEG discrimination with fast reconstructable CNN driven by reinforcement learning: A case study of ASD evaluation publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.04.009 – volume: 15 issue: 3 year: 2018 ident: 10.1016/j.neucom.2025.130353_b23 article-title: A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update publication-title: J. Neural Eng. doi: 10.1088/1741-2552/aab2f2 – start-page: 218 year: 2018 ident: 10.1016/j.neucom.2025.130353_b29 article-title: EEG based directional signal classification using RNN variants – volume: 260 year: 2025 ident: 10.1016/j.neucom.2025.130353_b15 article-title: Multimodal autism detection: Deep hybrid model with improved feature level fusion publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2024.108492 – volume: 7 start-page: 1239 issue: 12 year: 2017 ident: 10.1016/j.neucom.2025.130353_b25 article-title: Review and classification of emotion recognition based on EEG brain-computer interface system research: a systematic review publication-title: Appl. Sci. doi: 10.3390/app7121239 – volume: 269 year: 2023 ident: 10.1016/j.neucom.2025.130353_b8 article-title: Wireless EEG: A survey of systems and studies publication-title: NeuroImage doi: 10.1016/j.neuroimage.2022.119774 – volume: 620 year: 2025 ident: 10.1016/j.neucom.2025.130353_b20 article-title: ARNN: Attentive recurrent neural network for multi-channel EEG signals to identify epileptic seizures publication-title: Neurocomputing doi: 10.1016/j.neucom.2024.129203 – volume: 185 year: 2025 ident: 10.1016/j.neucom.2025.130353_b3 article-title: DCSENets: Interpretable deep learning for patient-independent seizure classification using enhanced EEG-based spectrogram visualization publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2024.109558 – volume: 250 year: 2024 ident: 10.1016/j.neucom.2025.130353_b28 article-title: Autism spectrum disorder diagnosis with EEG signals using time series maps of brain functional connectivity and a combined CNN–LSTM model publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2024.108196 – volume: 228 year: 2023 ident: 10.1016/j.neucom.2025.130353_b41 article-title: Deep learning based active learning technique for data annotation and improve the overall performance of classification models publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2023.120391 – start-page: 401 year: 2019 ident: 10.1016/j.neucom.2025.130353_b27 article-title: A hybrid model based on fuzzy rules to act on the diagnosed of autism in adults – volume: 13 start-page: 32 year: 2019 ident: 10.1016/j.neucom.2025.130353_b1 article-title: A brief history of simulation neuroscience publication-title: Front. Neuroinformatics doi: 10.3389/fninf.2019.00032 – volume: 134 year: 2021 ident: 10.1016/j.neucom.2025.130353_b37 article-title: Automated ASD detection using hybrid deep lightweight features extracted from EEG signals publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2021.104548 – volume: 1 start-page: 29 year: 2021 ident: 10.1016/j.neucom.2025.130353_b19 article-title: Review of the emotional feature extraction and classification using EEG signals publication-title: Cogn. Robot. doi: 10.1016/j.cogr.2021.04.001 – volume: 21 issue: 6 year: 2024 ident: 10.1016/j.neucom.2025.130353_b5 article-title: Temporal attention fusion network with custom loss function for EEG–fNIRS classification publication-title: J. Neural Eng. doi: 10.1088/1741-2552/ad8e86 – volume: 10 start-page: 7453 issue: 21 year: 2020 ident: 10.1016/j.neucom.2025.130353_b4 article-title: Electroencephalography (EEG) technology applications and available devices publication-title: Appl. Sci. doi: 10.3390/app10217453 |
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