{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T14:02:38Z","timestamp":1788962558298,"version":"build-2803163510"},"reference-count":28,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T00:00:00Z","timestamp":1674864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Electroencephalography (EEG) signal analysis is a rapid, low-cost, and practical method for diagnosing the early stages of dementia, including mild cognitive impairment (MCI) and Alzheimer\u2019s disease (AD). The extraction of appropriate biomarkers to assess a subject\u2019s cognitive impairment has attracted a lot of attention in recent years. The aberrant progression of AD leads to cortical detachment. Due to the interaction of several brain areas, these disconnections may show up as abnormalities in functional connectivity and complicated behaviors. Methods: This work suggests a novel method for differentiating between AD, MCI, and HC in two-class and three-class classifications based on EEG signals. To solve the class imbalance, we employ EEG data augmentation techniques, such as repeating minority classes using variational autoencoders (VAEs), as well as traditional noise-addition methods and hybrid approaches. The power spectrum density (PSD) and temporal data employed in this study\u2019s feature extraction from EEG signals were combined, and a support vector machine (SVM) classifier was used to distinguish between three categories of problems. Results: Insufficient data and unbalanced datasets are two common problems in AD datasets. This study has shown that it is possible to generate comparable data using noise addition and VAE, train the model using these data, and, to some extent, overcome the aforementioned issues with an increase in classification accuracy of 2 to 7%. Conclusion: In this work, using EEG data, we were able to successfully detect three classes: AD, MCI, and HC. In comparison to the pre-augmentation stage, the accuracy gained in the classification of the three classes increased by 3% when the VAE model added additional data. As a result, it is clear how useful EEG data augmentation methods are for classes with smaller sample numbers.<\/jats:p>","DOI":"10.3390\/diagnostics13030477","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T08:28:23Z","timestamp":1675067303000},"page":"477","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["An Approach toward Artificial Intelligence Alzheimer\u2019s Disease Diagnosis Using Brain Signals"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6197-3410","authenticated-orcid":false,"given":"Seyed-Ali","family":"Sadegh-Zadeh","sequence":"first","affiliation":[{"name":"Department of Computing, School of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent ST4 2DE, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elham","family":"Fakhri","sequence":"additional","affiliation":[{"name":"Department of Computing, School of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent ST4 2DE, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahboobe","family":"Bahrami","sequence":"additional","affiliation":[{"name":"Behavioral Sciences Research Center, School of Medicine, Isfahan University of Medical Sciences, Isfahan 8174533871, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elnaz","family":"Bagheri","sequence":"additional","affiliation":[{"name":"Department of Computing, School of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent ST4 2DE, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Razieh","family":"Khamsehashari","sequence":"additional","affiliation":[{"name":"Quality and Usability, Technical University of Berlin, 10623 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maryam","family":"Noroozian","sequence":"additional","affiliation":[{"name":"Cognitive Neurology and Neuropsychiatry Division, Department of Psychiatry, Tehran University of Medical Sciences, Tehran 1416634793, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6666-7866","authenticated-orcid":false,"given":"Amir M.","family":"Hajiyavand","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham B15 2SQ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1016\/j.freeradbiomed.2021.07.013","article-title":"Defective mitophagy and synaptic degeneration in Alzheimer\u2019s disease: Focus on aging, mitochondria and synapse","volume":"172","author":"Morton","year":"2021","journal-title":"Free Radic. 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