{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T15:29:58Z","timestamp":1777303798502,"version":"3.51.4"},"reference-count":21,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,5,27]],"date-time":"2023-05-27T00:00:00Z","timestamp":1685145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Recently, there has been a growing research interest in utilizing the electroencephalogram (EEG) as a non-invasive diagnostic tool for neurodegenerative diseases. This article provides a detailed description of a resting-state EEG dataset of individuals with Alzheimer\u2019s disease and frontotemporal dementia, and healthy controls. The dataset was collected using a clinical EEG system with 19 scalp electrodes while participants were in a resting state with their eyes closed. The data collection process included rigorous quality control measures to ensure data accuracy and consistency. The dataset contains recordings of 36 Alzheimer\u2019s patients, 23 frontotemporal dementia patients, and 29 healthy age-matched subjects. For each subject, the Mini-Mental State Examination score is reported. A monopolar montage was used to collect the signals. A raw and preprocessed EEG is included in the standard BIDS format. For the preprocessed signals, established methods such as artifact subspace reconstruction and an independent component analysis have been employed for denoising. The dataset has significant reuse potential since Alzheimer\u2019s EEG Machine Learning studies are increasing in popularity and there is a lack of publicly available EEG datasets. The resting-state EEG data can be used to explore alterations in brain activity and connectivity in these conditions, and to develop new diagnostic and treatment approaches. Additionally, the dataset can be used to compare EEG characteristics between different types of dementia, which could provide insights into the underlying mechanisms of these conditions.<\/jats:p>","DOI":"10.3390\/data8060095","type":"journal-article","created":{"date-parts":[[2023,5,27]],"date-time":"2023-05-27T16:19:37Z","timestamp":1685204377000},"page":"95","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":210,"title":["A Dataset of Scalp EEG Recordings of Alzheimer\u2019s Disease, Frontotemporal Dementia and Healthy Subjects from Routine EEG"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0675-9088","authenticated-orcid":false,"given":"Andreas","family":"Miltiadous","sequence":"first","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9640-7005","authenticated-orcid":false,"given":"Katerina D.","family":"Tzimourta","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece"},{"name":"Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2009-4166","authenticated-orcid":false,"given":"Theodora","family":"Afrantou","sequence":"additional","affiliation":[{"name":"2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0254-6624","authenticated-orcid":false,"given":"Panagiotis","family":"Ioannidis","sequence":"additional","affiliation":[{"name":"2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4278-3301","authenticated-orcid":false,"given":"Nikolaos","family":"Grigoriadis","sequence":"additional","affiliation":[{"name":"2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece"}]},{"given":"Dimitrios G.","family":"Tsalikakis","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1503-8952","authenticated-orcid":false,"given":"Pantelis","family":"Angelidis","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece"}]},{"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, University of Western Macedonia, 50100 Kozani, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5604-3507","authenticated-orcid":false,"given":"Euripidis","family":"Glavas","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece"}]},{"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, 47100 Arta, Greece"}]},{"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, 47100 Arta, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,27]]},"reference":[{"key":"ref_1","first-page":"a006189","article-title":"Neuropathological Alterations in Alzheimer Disease","volume":"1","author":"Frosch","year":"2011","journal-title":"Cold Spring Harb. 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