A Dataset of Scalp EEG Recordings of Alzheimer’s Disease, Frontotemporal Dementia and Healthy Subjects from Routine EEG

• DOI: 10.3390/data8060095
• Publication Date: 2023-05-27T20:19:37Z
• Authors: Andreas Miltiadous; Katerina D. Tzimo...; Theodora Afrantou; Panagiotis Ioannidis; Nikolaos Grigoriadis; Dimitrios G. Tsal...; Pantelis Angelidis; Markos G. Tsipouras; Euripidis Glavas; Nikolaos Giannakeas; Alexandros T. Tza...
• Keywords: Artifact (error); EEG-fMRI

Recently, there has been a growing research interest in utilizing the electroencephalogram (EEG) as non-invasive diagnostic tool for neurodegenerative diseases. This article provides detailed description of resting-state EEG dataset individuals with Alzheimer’s disease and frontotemporal dementia, healthy controls. The was collected using clinical system 19 scalp electrodes while participants were resting state their eyes closed. data collection process included rigorous quality control measures to ensure accuracy consistency. contains recordings 36 patients, 23 dementia 29 age-matched subjects. For each subject, Mini-Mental State Examination score is reported. A monopolar montage used collect signals. raw preprocessed standard BIDS format. signals, established methods such artifact subspace reconstruction an independent component analysis have employed denoising. significant reuse potential since Machine Learning studies are increasing popularity lack publicly available datasets. can be explore alterations brain activity connectivity these conditions, develop new treatment approaches. Additionally, compare characteristics between different types which could provide insights into underlying mechanisms conditions.