A Dataset of Scalp EEG Recordings of Alzheimer’s Disease, Frontotemporal Dementia and Healthy Subjects from Routine EEG
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 de...
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| Format: | Article |
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MDPI AG
2023-05-01
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| Online Access: | https://www.mdpi.com/2306-5729/8/6/95 |
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| author | Andreas Miltiadous Katerina D. Tzimourta Theodora Afrantou Panagiotis Ioannidis Nikolaos Grigoriadis Dimitrios G. Tsalikakis Pantelis Angelidis Markos G. Tsipouras Euripidis Glavas Nikolaos Giannakeas Alexandros T. Tzallas |
| author_facet | Andreas Miltiadous Katerina D. Tzimourta Theodora Afrantou Panagiotis Ioannidis Nikolaos Grigoriadis Dimitrios G. Tsalikakis Pantelis Angelidis Markos G. Tsipouras Euripidis Glavas Nikolaos Giannakeas Alexandros T. Tzallas |
| author_sort | Andreas Miltiadous |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T02:35:42Z |
| format | Article |
| id | doaj.art-4d216608a6144c8393bcf60982268448 |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-11T02:35:42Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-4d216608a6144c8393bcf609822684482023-11-18T09:58:24ZengMDPI AGData2306-57292023-05-01869510.3390/data8060095A Dataset of Scalp EEG Recordings of Alzheimer’s Disease, Frontotemporal Dementia and Healthy Subjects from Routine EEGAndreas Miltiadous0Katerina D. Tzimourta1Theodora Afrantou2Panagiotis Ioannidis3Nikolaos Grigoriadis4Dimitrios G. Tsalikakis5Pantelis Angelidis6Markos G. Tsipouras7Euripidis Glavas8Nikolaos Giannakeas9Alexandros T. Tzallas10Department 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, 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 Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, 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, GreeceDepartment of Informatics and Telecommunications, University of Ioannina, 47100 Arta, GreeceRecently, 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.https://www.mdpi.com/2306-5729/8/6/95electroencephalographyroutine EEGAlzheimer’s diseasefrontotemporal dementiaresting state |
| spellingShingle | Andreas Miltiadous Katerina D. Tzimourta Theodora Afrantou Panagiotis Ioannidis Nikolaos Grigoriadis Dimitrios G. Tsalikakis Pantelis Angelidis Markos G. Tsipouras Euripidis Glavas Nikolaos Giannakeas Alexandros T. Tzallas A Dataset of Scalp EEG Recordings of Alzheimer’s Disease, Frontotemporal Dementia and Healthy Subjects from Routine EEG Data electroencephalography routine EEG Alzheimer’s disease frontotemporal dementia resting state |
| title | A Dataset of Scalp EEG Recordings of Alzheimer’s Disease, Frontotemporal Dementia and Healthy Subjects from Routine EEG |
| title_full | A Dataset of Scalp EEG Recordings of Alzheimer’s Disease, Frontotemporal Dementia and Healthy Subjects from Routine EEG |
| title_fullStr | A Dataset of Scalp EEG Recordings of Alzheimer’s Disease, Frontotemporal Dementia and Healthy Subjects from Routine EEG |
| title_full_unstemmed | A Dataset of Scalp EEG Recordings of Alzheimer’s Disease, Frontotemporal Dementia and Healthy Subjects from Routine EEG |
| title_short | A Dataset of Scalp EEG Recordings of Alzheimer’s Disease, Frontotemporal Dementia and Healthy Subjects from Routine EEG |
| title_sort | dataset of scalp eeg recordings of alzheimer s disease frontotemporal dementia and healthy subjects from routine eeg |
| topic | electroencephalography routine EEG Alzheimer’s disease frontotemporal dementia resting state |
| url | https://www.mdpi.com/2306-5729/8/6/95 |
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