{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T06:16:29Z","timestamp":1778220989854,"version":"3.51.4"},"reference-count":51,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Neurosci."],"abstract":"<jats:p>Alzheimer\u2019s Disease (AD) is a neurodegenerative disorder with insidious onset, making early diagnosis challenging. Electroencephalogram (EEG) is a promising noninvasive tool for AD diagnosis, but high-density EEG configurations cause computational burdens and hinder clinical translation. Thus, developing an efficient sparse EEG channel selection method with high classification accuracy is urgent for AD auxiliary diagnosis. This study proposes a multi-strategy enhanced Whale Optimization Algorithm-Grey Wolf Optimizer (WOA-GWO) hybrid model for EEG channel selection, combined with a nonlinear dynamic feature fusion framework. We extracted geometric features from second-order difference plot (SODP) and complexity features (sample entropy, fuzzy entropy) of EEG signals, then adopted the ReliefF algorithm for feature fusion and key feature selection. The WOA-GWO model was improved via chaotic initialization, nonlinear convergence factors, spiral-hierarchical position update, and random perturbation to avoid local optima. Experimental results show that the proposed framework achieves a classification accuracy of 96.97% for AD detection, with significantly reduced EEG channel dimensions (four optimal channels identified: T5, FP1, T4, F4). The WOA-GWO model outperforms the original WOA and GWO in convergence speed and optimization accuracy, and the fused features exhibit strong discriminability for AD-related EEG abnormalities. This work provides a reliable computational framework for developing lightweight, portable AD diagnostic systems, and the identified optimal EEG channels offer neurophysiological evidence for AD electrophysiological biomarkers.<\/jats:p>","DOI":"10.3389\/fncom.2026.1835802","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T05:50:21Z","timestamp":1778219421000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Feature fusion and WOA-GWO optimization for Alzheimer\u2019s disease detection with sparse EEG channels"],"prefix":"10.3389","volume":"20","author":[{"given":"Ruofan","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information Technology Engineering, Tianjin University of Technology and Education","place":["Tianjin, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jitong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Technology Engineering, Tianjin University of Technology and Education","place":["Tianjin, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaxuan","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Computational Biology, Imperial College London","place":["London, United Kingdom"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siqian","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Medical Technology, Tianjin Medical University","place":["Tianjin, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zixuan","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Information Technology Engineering, Tianjin University of Technology and Education","place":["Tianjin, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanqiu","family":"Che","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Information Sensing and Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education","place":["Tianjin, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,8]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1016\/j.bspc.2011.07.007","article-title":"Automated diagnosis of epileptic EEG using entropies","volume":"7","author":"Acharya","year":"2012","journal-title":"Biomed. 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Bioelectron."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"212","DOI":"10.3390\/e28020212","article-title":"Multi-entropy feature concatenation for data-efficient cross-subject classification of Alzheimer\u2019s disease and frontotemporal dementia from single-channel EEG","volume":"28","author":"Li","year":"2026","journal-title":"Entropy"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"22991","DOI":"10.1109\/ACCESS.2023.3304889","article-title":"Recent advances in grey wolf optimizer, its versions and applications","volume":"12","author":"Makhadmeh","year":"2023","journal-title":"IEEE Access"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"1713472","DOI":"10.3389\/fnagi.2026.1713472","article-title":"Multimodal neuroimaging discrimination of Alzheimer\u2019s disease, mild cognitive impairment, and late-life depression using electroencephalography and functional near-infrared spectroscopy: integrating electrophysiological and hemodynamic biomarkers","volume":"18","author":"Mei","year":"2026","journal-title":"Front. 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Homepage Httpiieta Orgjournalsijsse"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"4113","DOI":"10.1007\/s11831-023-09928-7","article-title":"A systematic review of the whale optimization algorithm: theoretical foundation, improvements, and hybridizations","volume":"30","author":"Nadimi-Shahraki","year":"2023","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref36","doi-asserted-by":"publisher","first-page":"6639","DOI":"10.48550\/arXiv.1706.09516","article-title":"CatBoost: unbiased boosting with categorical features","volume":"31","author":"Prokhorenkova","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref37","doi-asserted-by":"publisher","first-page":"185","DOI":"10.14456\/mijet.2025.18","article-title":"Depression classification with imbalanced data problems: literature survey","volume":"11","author":"Rojarath","year":"2025","journal-title":"Eng. Access"},{"key":"ref38","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1186\/s12938-025-01482-6","article-title":"Deep learning for Alzheimer\u2019s disease: advances in classification, segmentation, subtyping, and explainability","volume":"24","author":"Shaikh","year":"2025","journal-title":"Biomed. Eng. Online"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"10219","DOI":"10.3390\/su131810219","article-title":"Unit commitment for power generation systems based on prices in smart grid environment considering uncertainty","volume":"13","author":"Shokouhandeh","year":"2021","journal-title":"Sustainability"},{"key":"ref40","doi-asserted-by":"publisher","first-page":"1506869","DOI":"10.3389\/fncom.2025.1506869","article-title":"EEG electrode setup optimization using feature extraction techniques for neonatal sleep state classification","volume":"19","author":"Siddiqa","year":"2025","journal-title":"Front. Comput. 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Tools Appl."},{"key":"ref42","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1007\/978-3-032-04558-4_34","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2025","author":"Su\u00e1rez-Marcote","year":"2026"},{"key":"ref43","doi-asserted-by":"publisher","first-page":"8078","DOI":"10.3390\/s23198078","article-title":"Epileptic EEG signal detection using variational modal decomposition and improved grey wolf algorithm","volume":"23","author":"Sun","year":"2023","journal-title":"Sensors"},{"key":"ref44","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.jbi.2018.07.014","article-title":"Relief-based feature selection: introduction and review","volume":"85","author":"Urbanowicz","year":"2018","journal-title":"J. Biomed. Inform."},{"key":"ref45","doi-asserted-by":"publisher","first-page":"2993","DOI":"10.1007\/s11571-024-10130-z","article-title":"Automatic detection of Alzheimer\u2019s disease from EEG signals using an improved AFS\u2013GA hybrid algorithm","volume":"18","author":"Wang","year":"2024","journal-title":"Cogn. Neurodyn."},{"key":"ref46","doi-asserted-by":"publisher","first-page":"1540","DOI":"10.3390\/e24111540","article-title":"Epileptic seizure detection using geometric features extracted from SODP shape of EEG signals and AsyLnCPSO-GA","volume":"24","author":"Wang","year":"2022","journal-title":"Entropy"},{"key":"ref47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11571-025-10348-5","article-title":"Research on the classification of EEG signals for dementia and its interpretability using the GWOCS agorithm","volume":"20","author":"Wang","year":"2026","journal-title":"Cogn. Neurodyn."},{"key":"ref48","doi-asserted-by":"publisher","first-page":"3536","DOI":"10.3390\/su17083536","article-title":"Assessment of landslide susceptibility based on ReliefF feature weight fusion: a case study of Wenxian County, Longnan City","volume":"17","author":"Wang","year":"2025","journal-title":"Sustainability"},{"key":"ref49","doi-asserted-by":"publisher","first-page":"156","DOI":"10.3390\/ijgi6060156","article-title":"An efficient vector-raster overlay algorithm for high-accuracy and high-efficiency surface area calculations of irregularly shaped land use patches","volume":"6","author":"Xie","year":"2017","journal-title":"ISPRS Int. J. Geo Inf."},{"key":"ref50","doi-asserted-by":"publisher","first-page":"183","DOI":"10.3390\/machines13030183","article-title":"An improved fault diagnosis method for rolling bearing based on relief-F and optimized random forests algorithm","volume":"13","author":"Yang","year":"2025","journal-title":"Machines"},{"key":"ref51","doi-asserted-by":"publisher","first-page":"1522552","DOI":"10.3389\/fnagi.2025.1522552","article-title":"The role of quantitative EEG biomarkers in Alzheimer\u2019s disease and mild cognitive impairment: applications and insights","volume":"17","author":"Yuan","year":"2025","journal-title":"Front. 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