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

Andreas Miltiadous, Katerina D. Tzimourta, Theodora Afrantou, Panagiotis Ioannidis, Nikolaos Grigoriadis, Dimitrios G. Tsalikakis, Pantelis Angelidis, Markos G. Tsipouras, Euripidis Glavas, Nikolaos Giannakeas and Alexandros T. Tzallas ()
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Andreas Miltiadous: Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece
Katerina D. Tzimourta: Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece
Theodora Afrantou: 2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
Panagiotis Ioannidis: 2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
Nikolaos Grigoriadis: 2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
Dimitrios G. Tsalikakis: Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece
Pantelis Angelidis: Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece
Markos G. Tsipouras: Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece
Euripidis Glavas: Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece
Nikolaos Giannakeas: Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece
Alexandros T. Tzallas: Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece

Data, 2023, vol. 8, issue 6, 1-10

Abstract: 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.

Keywords: electroencephalography; routine EEG; Alzheimer’s disease; frontotemporal dementia; resting state (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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