{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T05:03:16Z","timestamp":1779771796348,"version":"3.53.1"},"reference-count":26,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T00:00:00Z","timestamp":1779321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Electroencephalography (EEG) has emerged as a promising non-invasive tool for the diagnosis of neurodegenerative disorders, and artificial intelligence (AI) has shown significant potential in this domain, as demonstrated by recent studies. However, strong inter-subject variability remains a major challenge, limiting the ability of AI-based models to learn disease-specific features that generalize across individuals, thereby hindering the development of clinically deployable subject-independent systems. In this work, we propose a cross-subject, AI-based EEG classification framework to distinguish between Alzheimer\u2019s disease (AD), Creutzfeldt\u2013Jakob disease (CJD), and healthy control subjects using clinical EEG data collected from a local hospital. A lightweight hybrid deep learning model is developed, combining a two-layer one-dimensional convolutional neural network with a two-layer Transformer encoder to capture both local temporal patterns and long-range dependencies in EEG signals. The proposed model achieves an average classification accuracy of 97%, representing a 3% improvement over a baseline model evaluated on a cohort of 36 subjects. To assess deployment feasibility in real-time clinical settings, the trained model is implemented and evaluated on an edge-AI platform (NVIDIA Jetson AGX Orin), demonstrating energy efficiency for the inference with a compact model footprint. These results indicate that the proposed approach provides an accurate, efficient, and practically deployable solution for subject-independent EEG-based classification of neurological disorders.<\/jats:p>","DOI":"10.3390\/s26103274","type":"journal-article","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T15:02:27Z","timestamp":1779375747000},"page":"3274","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An EEG-Based Edge-AI Framework for Alzheimer\u2019s and Creutzfeldt\u2013Jakob Disease Classification"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1946-285X","authenticated-orcid":false,"given":"Muhammad","family":"Suffian","sequence":"first","affiliation":[{"name":"DICEAM, Mediterranea University of Reggio Calabria, 89125 Reggio Calabria, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cosimo","family":"Ieracitano","sequence":"additional","affiliation":[{"name":"DICMaPI, University of Naples \u201cFederico II\u201d, 80125 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4962-3500","authenticated-orcid":false,"given":"Nadia","family":"Mammone","sequence":"additional","affiliation":[{"name":"DICEAM, Mediterranea University of Reggio Calabria, 89125 Reggio Calabria, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0085-9494","authenticated-orcid":false,"given":"Angelo","family":"Pascarella","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences, Magna Gr\u00e6cia University of Catanzaro, 88100 Catanazaro, Italy"},{"name":"Regional Epilepsy Centre, Great Metropolitan \u201cBianchi-Melacrino-Morelli\u201d Hospital, 89124 Reggio Calabria, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edoardo","family":"Ferlazzo","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences, Magna Gr\u00e6cia University of Catanzaro, 88100 Catanazaro, Italy"},{"name":"Regional Epilepsy Centre, Great Metropolitan \u201cBianchi-Melacrino-Morelli\u201d Hospital, 89124 Reggio Calabria, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0734-9136","authenticated-orcid":false,"given":"Francesco Carlo","family":"Morabito","sequence":"additional","affiliation":[{"name":"DICEAM, Mediterranea University of Reggio Calabria, 89125 Reggio Calabria, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e70002","DOI":"10.1002\/widm.70002","article-title":"Automated Detection of Neurological and Mental Health Disorders Using EEG Signals and Artificial Intelligence: A Systematic Review","volume":"15","author":"Uyanik","year":"2025","journal-title":"Wiley Interdiscip. 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