A Complementary Dataset of Scalp EEG Recordings Featuring Participants with Alzheimer’s Disease, Frontotemporal Dementia, and Healthy Controls, Obtained from Photostimulation EEG

• Published in: Data (Basel) Vol. 10; no. 5; p. 64
• Main Authors: Ntetska, Aimilia, Miltiadous, Andreas, Tsipouras, Markos G., Tzimourta, Katerina D., Afrantou, Theodora, Ioannidis, Panagiotis, Tsalikakis, Dimitrios G., Sakkas, Konstantinos, Oikonomou, Emmanouil D., Grigoriadis, Nikolaos, Angelidis, Pantelis, Giannakeas, Nikolaos, Tzallas, Alexandros T.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2025
• Subjects: Algorithms; Alzheimer's disease; Biological markers; Biomarkers; Brain; Brain research; Cognitive ability; Data structures; Datasets; Degeneration; Dementia; Disease; Electroencephalography; frontotemporal dementia; Independent component analysis; Interoperability; Language disorders; Machine learning; Magnetic resonance imaging; Medical imaging; Memory; Nervous system; Neuroimaging; Neuropsychology; open eyes; photo-stimulation; routine EEG; Stimulation; Greece
• ISSN: 2306-5729; 2306-5729
• DOI: 10.3390/data10050064

Research interest in the application of electroencephalogram (EEG) as a non-invasive diagnostic tool for the automated detection of neurodegenerative diseases is growing. Open-access datasets have become crucial for researchers developing such methodologies. Our previously published open-access dataset of resting-state (eyes-closed) EEG recordings from patients with Alzheimer’s disease (AD), frontotemporal dementia (FTD), and cognitively normal (CN) controls has attracted significant attention. In this paper, we present a complementary dataset consisting of eyes-open photic stimulation recordings from the same cohort. The dataset includes recordings from 88 participants (36 AD, 23 FTD, and 29 CN) and is provided in Brain Imaging Data Structure (BIDS) format, promoting consistency and ease of use across research groups. Additionally, a fully preprocessed version is included, using EEGLAB-based pipelines that involve filtering, artifact removal, and Independent Component Analysis, preparing the data for machine learning applications. This new dataset enables the study of brain responses to visual stimulation across different cognitive states and supports the development and validation of automated classification algorithms for dementia detection. It offers a valuable benchmark for both methodological comparisons and biological investigations, and it is expected to significantly contribute to the fields of neurodegenerative disease research, biomarker discovery, and EEG-based diagnostics.