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

• Published in: Data (Basel) Vol. 8; no. 6; p. 95
• Main Authors: Miltiadous, Andreas, Tzimourta, Katerina D., Afrantou, Theodora, Ioannidis, Panagiotis, Grigoriadis, Nikolaos, Tsalikakis, Dimitrios G., Angelidis, Pantelis, Tsipouras, Markos G., Glavas, Euripidis, Giannakeas, Nikolaos, Tzallas, Alexandros T.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2023
• Subjects: Alzheimer's disease; Brain research; Data collection; Datasets; Dementia; Electroencephalography; frontotemporal dementia; Illnesses; Independent component analysis; Machine learning; Magnetic resonance imaging; Medical imaging; Metadata; Neuroimaging; Neuropsychology; Open source software; Physiological aspects; Quality control; resting state; routine EEG; Greece
• DOI: 10.3390/data8060095
• ISSN: 2306-5729

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’s 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’s 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’s 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.