{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T16:41:37Z","timestamp":1791909697962,"version":"4.3.4"},"reference-count":54,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T00:00:00Z","timestamp":1711670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science Foundation Ireland Centre for Research Training in Digitally-Enhanced Reality (d-real)","award":["18\/CRT\/6224"],"award-info":[{"award-number":["18\/CRT\/6224"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sciences"],"abstract":"<jats:p>Early-stage Alzheimer\u2019s disease (AD) and frontotemporal dementia (FTD) share similar symptoms, complicating their diagnosis and the development of specific treatment strategies. Our study evaluated multiple feature extraction techniques for identifying AD and FTD biomarkers from electroencephalographic (EEG) signals. We developed an optimised machine learning architecture that integrates sliding windowing, feature extraction, and supervised learning to distinguish between AD and FTD patients, as well as from healthy controls (HCs). Our model, with a 90% overlap for sliding windowing, SVD entropy for feature extraction, and K-Nearest Neighbors (KNN) for supervised learning, achieved a mean F1-score and accuracy of 93% and 91%, 92.5% and 93%, and 91.5% and 91% for discriminating AD and HC, FTD and HC, and AD and FTD, respectively. The feature importance array, an explainable AI feature, highlighted the brain lobes that contributed to identifying and distinguishing AD and FTD biomarkers. This research introduces a novel framework for detecting and discriminating AD and FTD using EEG signals, addressing the need for accurate early-stage diagnostics. Furthermore, a comparative evaluation of sliding windowing, multiple feature extraction, and machine learning methods on AD\/FTD detection and discrimination is documented.<\/jats:p>","DOI":"10.3390\/brainsci14040335","type":"journal-article","created":{"date-parts":[[2024,3,31]],"date-time":"2024-03-31T13:39:37Z","timestamp":1711892377000},"page":"335","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer\u2019s Disease with Electroencephalography in Resting-State Adults"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2133-8126","authenticated-orcid":false,"given":"Utkarsh","family":"Lal","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal 576104, Karnataka, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6472-1339","authenticated-orcid":false,"given":"Arjun Vinayak","family":"Chikkankod","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India"},{"name":"Artificial Intelligence and Cognitive Load Lab, the Applied Intelligence Research Centre, Technological University Dublin, D07 H6K8 Dublin, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2718-5426","authenticated-orcid":false,"given":"Luca","family":"Longo","sequence":"additional","affiliation":[{"name":"Artificial Intelligence and Cognitive Load Lab, the Applied Intelligence Research Centre, Technological University Dublin, D07 H6K8 Dublin, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Breijyeh, Z., and Karaman, R. 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