A Complementary Dataset of Scalp EEG Recordings Featuring Participants with Alzheimer’s Disease, Frontotemporal Dementia, and Healthy Controls, Obtained from Photostimulation EEG
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 data...
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2025-04-01
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| author | 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 Alexandros T. Tzallas |
| author_facet | 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 Alexandros T. Tzallas |
| author_sort | Aimilia Ntetska |
| collection | DOAJ |
| description | 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. |
| format | Article |
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| institution | OA Journals |
| issn | 2306-5729 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj-art-9fb42843dc264fcda596645d14f44a482025-08-20T02:33:44ZengMDPI AGData2306-57292025-04-011056410.3390/data10050064A Complementary Dataset of Scalp EEG Recordings Featuring Participants with Alzheimer’s Disease, Frontotemporal Dementia, and Healthy Controls, Obtained from Photostimulation EEGAimilia Ntetska0Andreas Miltiadous1Markos G. Tsipouras2Katerina D. Tzimourta3Theodora Afrantou4Panagiotis Ioannidis5Dimitrios G. Tsalikakis6Konstantinos Sakkas7Emmanouil D. Oikonomou8Nikolaos Grigoriadis9Pantelis Angelidis10Nikolaos Giannakeas11Alexandros T. Tzallas12Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, GreeceDepartment of Informatics and Telecommunications, University of Ioannina, 47100 Arta, GreeceDepartment of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, GreeceDepartment of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, 54636 Thessaloniki, GreeceDepartment of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, GreeceDepartment of Informatics and Telecommunications, University of Ioannina, 47100 Arta, GreeceDepartment of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, 54636 Thessaloniki, GreeceDepartment of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, GreeceDepartment of Informatics and Telecommunications, University of Ioannina, 47100 Arta, GreeceDepartment of Informatics and Telecommunications, University of Ioannina, 47100 Arta, GreeceResearch 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.https://www.mdpi.com/2306-5729/10/5/64electroencephalographyroutine EEGAlzheimer’s diseasefrontotemporal dementiaopen eyesphoto-stimulation |
| spellingShingle | 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 Alexandros T. Tzallas A Complementary Dataset of Scalp EEG Recordings Featuring Participants with Alzheimer’s Disease, Frontotemporal Dementia, and Healthy Controls, Obtained from Photostimulation EEG Data electroencephalography routine EEG Alzheimer’s disease frontotemporal dementia open eyes photo-stimulation |
| title | A Complementary Dataset of Scalp EEG Recordings Featuring Participants with Alzheimer’s Disease, Frontotemporal Dementia, and Healthy Controls, Obtained from Photostimulation EEG |
| title_full | A Complementary Dataset of Scalp EEG Recordings Featuring Participants with Alzheimer’s Disease, Frontotemporal Dementia, and Healthy Controls, Obtained from Photostimulation EEG |
| title_fullStr | A Complementary Dataset of Scalp EEG Recordings Featuring Participants with Alzheimer’s Disease, Frontotemporal Dementia, and Healthy Controls, Obtained from Photostimulation EEG |
| title_full_unstemmed | A Complementary Dataset of Scalp EEG Recordings Featuring Participants with Alzheimer’s Disease, Frontotemporal Dementia, and Healthy Controls, Obtained from Photostimulation EEG |
| title_short | A Complementary Dataset of Scalp EEG Recordings Featuring Participants with Alzheimer’s Disease, Frontotemporal Dementia, and Healthy Controls, Obtained from Photostimulation EEG |
| title_sort | complementary dataset of scalp eeg recordings featuring participants with alzheimer s disease frontotemporal dementia and healthy controls obtained from photostimulation eeg |
| topic | electroencephalography routine EEG Alzheimer’s disease frontotemporal dementia open eyes photo-stimulation |
| url | https://www.mdpi.com/2306-5729/10/5/64 |
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