{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T09:14:27Z","timestamp":1792142067716,"version":"4.4.0"},"reference-count":62,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T00:00:00Z","timestamp":1736812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>Background: Alzheimer\u2019s disease (AD) is a progressive neurodegenerative disorder that poses critical challenges in global healthcare due to its increasing prevalence and severity. Diagnosing AD and other dementias, such as frontotemporal dementia (FTD), is slow and resource-intensive, underscoring the need for automated approaches. Methods: To address this gap, this study proposes a novel deep learning methodology for EEG classification of AD, FTD, and control (CN) signals. The approach incorporates advanced preprocessing techniques and CNN classification of FFT-based spectrograms and is evaluated using the leave-N-subjects-out validation, ensuring robust cross-subject generalizability. Results: The results indicate that the proposed methodology outperforms state-of-the-art machine learning and EEG-specific neural network models, achieving an accuracy of 79.45% for AD\/CN classification and 80.69% for AD+FTD\/CN classification. Conclusions: These results highlight the potential of EEG-based deep learning models for early dementia screening, enabling more efficient, scalable, and accessible diagnostic tools.<\/jats:p>","DOI":"10.3390\/jpm15010027","type":"journal-article","created":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T03:29:43Z","timestamp":1736825383000},"page":"27","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["A Novel CNN-Based Framework for Alzheimer\u2019s Disease Detection Using EEG Spectrogram Representations"],"prefix":"10.3390","volume":"15","author":[{"given":"Konstantinos","family":"Stefanou","sequence":"first","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, Kostakioi, 47100 Arta, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9640-7005","authenticated-orcid":false,"given":"Katerina D.","family":"Tzimourta","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Faculty of Engineering, University of Western Macedonia, 50100 Kozani, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5638-7580","authenticated-orcid":false,"given":"Christos","family":"Bellos","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, Kostakioi, 47100 Arta, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georgios","family":"Stergios","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, Kostakioi, 47100 Arta, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konstantinos","family":"Markoglou","sequence":"additional","affiliation":[{"name":"School of Science & Technology, Hellenic Open University, 26335 Patra, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emmanouil","family":"Gionanidis","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, Kostakioi, 47100 Arta, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6757-1698","authenticated-orcid":false,"given":"Markos G.","family":"Tsipouras","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Faculty of Engineering, University of Western Macedonia, 50100 Kozani, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0615-783X","authenticated-orcid":false,"given":"Nikolaos","family":"Giannakeas","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, Kostakioi, 47100 Arta, Greece"},{"name":"School of Science & Technology, Hellenic Open University, 26335 Patra, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9043-1290","authenticated-orcid":false,"given":"Alexandros T.","family":"Tzallas","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, Kostakioi, 47100 Arta, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0675-9088","authenticated-orcid":false,"given":"Andreas","family":"Miltiadous","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, Kostakioi, 47100 Arta, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.mcna.2018.10.009","article-title":"The Alzheimer\u2019s Disease Clinical Spectrum","volume":"103","author":"Atri","year":"2019","journal-title":"Med. 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