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Neural Eng."],"published-print":{"date-parts":[[2026,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental condition characterized by significant inter-subject variability in electroencephalogram (EEG) features. Existing deep learning approaches often fail to fully capture intrinsic spatio-temporal dependencies or rely on static feature fusion strategies, struggling to characterize complex non-Euclidean topological abnormalities. We aim to address these challenges by proposing a robust and adaptive diagnostic framework.\n                    <jats:italic>Approach.<\/jats:italic>\n                    We propose HFG-Net, a high-frequency guided spatio-temporal synergistic network. This framework incorporates three core innovations: First, a channel-temporal multi-scale attention mechanism captures transient spatio-temporal coupling. Second, a high-frequency guided multi-view graph construction strategy leverages sparse skeletons from beta\/gamma bands to filter all-band Pearson correlation and phase locking value matrices, constructing noise-resistant topologies. Third, a dynamic synergistic fusion module employs a gating network for sample-level adaptive feature integration.\n                    <jats:italic>Main results.<\/jats:italic>\n                    Experiments on a clinical dataset of 120 subjects demonstrate that HFG-Net achieves a classification accuracy of 95.79% and an F1-score of 92.89% on an independent test set. The model further achieves a recognition rate of 98.43% for patients with mild ASD.\n                    <jats:italic>Significance.<\/jats:italic>\n                    Interpretability analysis reveals that the model\u2019s focus on high-frequency abnormal connectivity aligns with neuropathological findings. HFG-Net effectively addresses the challenges of synergistic spatio-temporal modeling and heterogeneity adaptation, providing an efficient, robust, and interpretable paradigm for EEG-based diagnosis.\n                  <\/jats:p>","DOI":"10.1088\/1741-2552\/ae9190","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T22:51:25Z","timestamp":1785279085000},"page":"046048","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["HFG-Net: high-frequency guided multi-view graph convolution and dynamic spatio-temporal fusion for ASD diagnosis"],"prefix":"10.1088","volume":"23","author":[{"given":"Jiannan","family":"Kang","sequence":"first","affiliation":[{"name":"Hebei University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4952-6959","authenticated-orcid":true,"given":"Zhiyuan","family":"Fan","sequence":"additional","affiliation":[{"name":"Hebei University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongbing","family":"Xiao","sequence":"additional","affiliation":[{"name":"Hebei University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hebei University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1359-5130","authenticated-orcid":true,"given":"Xiaoli","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Normal University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2425-7551","authenticated-orcid":true,"given":"Yue","family":"Gu","sequence":"additional","affiliation":[{"name":"Tianjin University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"key":"jneae9190bib1","author":"American Psychiatric Association","year":"2013","edition":"5th edn","type":"book"},{"key":"jneae9190bib2","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/s40810-014-0001-5","type":"journal-article","article-title":"EEG changes associated with autistic spectrum disorders","volume":"1","author":"Boutros","year":"2015","journal-title":"Neuropsychiatr. 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