{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T12:02:19Z","timestamp":1785412939028,"version":"3.56.0"},"reference-count":31,"publisher":"IMR Press","issue":"7","license":[{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Program of Natural Science Foundation of Zhejiang Province","award":["LZ24F010007"],"award-info":[{"award-number":["LZ24F010007"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62271177"],"award-info":[{"award-number":["62271177"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Integr. Neurosci."],"abstract":"<jats:p>Background:Brain source localization technology enables precise characterization of the spatial distribution of neural activity, serving as a crucial tool for exploring the pathological mechanisms underlying dementia. However, effectively integrating complementary diagnostic information from source localization features across multiple frequency bands remains a major challenge to enhancing classification performance and model interpretability.Methods:An attention-based multi-frequency self-supervised fusion model (AM-SSF) is proposed to address this issue. Independent contrastive self-supervised encoders are trained for the \u03b8 (4\u20138 Hz), \u03b1 (8\u201313 Hz), \u03b2 (13\u201330 Hz), and \u03b3 (30\u201348 Hz) frequency bands to learn band-specific latent representations. Then, an attention-guided adaptive fusion module is introduced to dynamically allocate band weights through cross-entropy\u2013based supervised optimization, thereby achieving effective cross-band information integration. Finally, a random forest classifier is employed to evaluate the model\u2019s performance in distinguishing Alzheimer\u2019s disease (AD) from frontotemporal dementia (FTD).Results:Experimental results show that the proposed framework achieves a classification accuracy of 93.1% under five-fold cross-validation, significantly outperforming baseline methods such as single-band self-supervised learning (SSL) and average pooling fusion. Further analysis of the attention weight distributions revealed that the \u03b8 and \u03b2 bands contributed most to model decision-making, providing interpretability regarding frequency-specific effects.Conclusions:In summary, the proposed AM-SSF model enhances AD and FTD classification performance while offering valuable insights into the discriminative roles of frequency band features.<\/jats:p>","DOI":"10.31083\/jin50737","type":"journal-article","created":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T11:01:26Z","timestamp":1785409286000},"source":"Crossref","is-referenced-by-count":0,"title":["A Multi-Frequency Self-Supervised Fusion Model for EEG-Based Dementia Classification"],"prefix":"10.31083","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7662-9126","authenticated-orcid":true,"given":"Jinhua","family":"Sheng","sequence":"first","affiliation":[{"name":"School of Computer Science, Hangzhou Dianzi University, 310018 Hangzhou, Zhejiang, China"},{"name":"Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, 310018 Hangzhou, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqi","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hangzhou Dianzi University, 310018 Hangzhou, Zhejiang, China"},{"name":"Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, 310018 Hangzhou, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Hospital, 100730 Beijing, China"},{"name":"National Center of Gerontology, 100730 Beijing, China"},{"name":"Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, 100730 Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaozhe","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hangzhou Dianzi University, 310018 Hangzhou, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hangzhou Dianzi University, 310018 Hangzhou, Zhejiang, China"},{"name":"Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, 310018 Hangzhou, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Wu","sequence":"additional","affiliation":[{"name":"Beijing Hospital, 100730 Beijing, China"},{"name":"National Center of Gerontology, 100730 Beijing, China"},{"name":"Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, 100730 Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhan","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hangzhou Dianzi University, 310018 Hangzhou, Zhejiang, China"},{"name":"Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, 310018 Hangzhou, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinrui","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hangzhou Dianzi University, 310018 Hangzhou, Zhejiang, China"},{"name":"Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, 310018 Hangzhou, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongyuan","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hangzhou Dianzi University, 310018 Hangzhou, Zhejiang, China"},{"name":"Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, 310018 Hangzhou, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"16242","published-online":{"date-parts":[[2026,7,21]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","unstructured":"Mollah SA, Nayak A, Barhai S, Maity U. 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