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

Aimilia Ntetska, Andreas Miltiadous, Markos G. Tsipouras, Katerina D. Tzimourta, Theodora Afrantou, Panagiotis Ioannidis, Dimitrios G. Tsalikakis, Konstantinos Sakkas, Emmanouil D. Oikonomou, Nikolaos Grigoriadis, Pantelis Angelidis, Nikolaos Giannakeas and Alexandros T. Tzallas ()
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Aimilia Ntetska: Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece
Andreas Miltiadous: Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece
Markos G. Tsipouras: Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece
Katerina D. Tzimourta: Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, 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
Dimitrios G. Tsalikakis: Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece
Konstantinos Sakkas: Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece
Emmanouil D. Oikonomou: Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece
Nikolaos Grigoriadis: 2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
Pantelis Angelidis: Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, 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, 2025, vol. 10, issue 5, 1-14

Abstract: 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.

Keywords: electroencephalography; routine EEG; Alzheimer’s disease; frontotemporal dementia; open eyes; photo-stimulation (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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