{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T06:05:33Z","timestamp":1784786733685,"version":"3.55.0"},"reference-count":96,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T00:00:00Z","timestamp":1784764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychiatry"],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This review aims to comprehensively review the methodologies and advancements in using electroencephalography (EEG) for the early diagnosis of Alzheimer\u2019s disease (AD), addressing the limitations of traditional diagnostic tools.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a comprehensive analysis of current research, encompassing the complete EEG analysis pipeline from signal acquisition and preprocessing to feature extraction\u2014including time-frequency and brain network metrics\u2014and the application of machine learning and deep learning algorithms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Characteristic EEG alterations, such as spectral slowing and reduced signal complexity, are associated with early AD. Advanced feature extraction combined with intelligent algorithms significantly enhances diagnostic performance, with some studies reporting classification accuracies exceeding 95%. Integration of EEG with multimodal data (e.g., MRI, genetic markers) further improves diagnostic robustness.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>EEG is a promising, non-invasive, and cost-effective tool for early AD detection in appropriate clinical and research settings. The integration of advanced signal processing with intelligent algorithms can significantly improves diagnostic precision, though clinical translation requires standardized protocols and validation on larger, diverse cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Significance<\/jats:title>\n                    <jats:p>This work highlights the potential of EEG to facilitate accessible, early-stage AD screening, which is crucial for timely intervention and may contribute to reducing the disease\u2019s future socioeconomic burden.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyt.2026.1819726","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T05:37:51Z","timestamp":1784785071000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Electroencephalography for early Alzheimer\u2019s disease diagnosis: from advanced feature engineering to interpretable ai and clinical translation"],"prefix":"10.3389","volume":"17","author":[{"given":"Mengjiao","family":"Chi","sequence":"first","affiliation":[{"name":"Zhejiang Chinese Medical University","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anqi","family":"Zhao","sequence":"additional","affiliation":[{"name":"Zhejiang Chinese Medical University","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang Chinese Medical University","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liping","family":"Fan","sequence":"additional","affiliation":[{"name":"The Second Affiliated Hospital of Zhejiang Chinese Medical University","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang Chinese Medical University","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bing","family":"Tang","sequence":"additional","affiliation":[{"name":"Zhejiang Chinese Medical University","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Tao","sequence":"additional","affiliation":[{"name":"Zhejiang Chinese Medical University","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,23]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/j.jalz.2016.03.001","article-title":"2021 Alzheimer\u2019s disease facts and figures","volume":"17","year":"2021","journal-title":"Alzheimers 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