Data Sheet 1_The use of low-density EEG for the classification of PPA and MCI.docx
Objective<p>Dissociating Primary Progressive Aphasia (PPA) from Mild Cognitive Impairment (MCI) is an important, yet challenging task. Given the need for low-cost and time-efficient classification, we used low-density electroencephalography (EEG) recordings to automatically classify PPA, MCI a...
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2025
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| _version_ | 1852022932090388480 |
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| author | Panteleimon Chriskos (17251084) |
| author2 | Kyriaki Neophytou (6377588) Christos A. Frantzidis (10780023) Jessica Gallegos (20688188) Alexandros Afthinos (13806361) Chiadi U. Onyike (20688191) Argye Hillis (10471100) Panagiotis D. Bamidis (9988646) Kyrana Tsapkini (6831107) |
| author2_role | author author author author author author author author |
| author_facet | Panteleimon Chriskos (17251084) Kyriaki Neophytou (6377588) Christos A. Frantzidis (10780023) Jessica Gallegos (20688188) Alexandros Afthinos (13806361) Chiadi U. Onyike (20688191) Argye Hillis (10471100) Panagiotis D. Bamidis (9988646) Kyrana Tsapkini (6831107) |
| author_role | author |
| dc.creator.none.fl_str_mv | Panteleimon Chriskos (17251084) Kyriaki Neophytou (6377588) Christos A. Frantzidis (10780023) Jessica Gallegos (20688188) Alexandros Afthinos (13806361) Chiadi U. Onyike (20688191) Argye Hillis (10471100) Panagiotis D. Bamidis (9988646) Kyrana Tsapkini (6831107) |
| dc.date.none.fl_str_mv | 2025-02-07T06:48:54Z |
| dc.identifier.none.fl_str_mv | 10.3389/fnhum.2025.1526554.s001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Data_Sheet_1_The_use_of_low-density_EEG_for_the_classification_of_PPA_and_MCI_docx/28367321 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Neurocognitive Patterns and Neural Networks primary progressive aphasia mild cognitive impairment classification electroencephalography (EEG) functional connectivity energy rhythms |
| dc.title.none.fl_str_mv | Data Sheet 1_The use of low-density EEG for the classification of PPA and MCI.docx |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | Objective<p>Dissociating Primary Progressive Aphasia (PPA) from Mild Cognitive Impairment (MCI) is an important, yet challenging task. Given the need for low-cost and time-efficient classification, we used low-density electroencephalography (EEG) recordings to automatically classify PPA, MCI and healthy control (HC) individuals. To the best of our knowledge, this is the first attempt to classify individuals from these three populations at the same time.</p>Methods<p>We collected three-minute EEG recordings with an 8-channel system from eight MCI, fourteen PPA and eight HC individuals. Utilizing the Relative Wavelet Entropy method, we derived (i) functional connectivity, (ii) graph theory metrics and extracted (iii) various energy rhythms. Features from all three sources were used for classification. The k-Nearest Neighbor and Support Vector Machines classifiers were used.</p>Results<p>A 100% individual classification accuracy was achieved in the HC-MCI, HC-PPA, and MCI-PPA comparisons, and a 77.78% accuracy in the HC-MCI-PPA comparison.</p>Conclusion<p>We showed for the first time that successful automatic classification between HC, MCI and PPA is possible with short, low-density EEG recordings. Despite methodological limitations of the current study, these results have important implications for clinical practice since they show that fast, low-cost and accurate disease diagnosis of these disorders is possible. Future studies need to establish the generalizability of the current findings with larger sample sizes and the efficient use of this methodology in a clinical setting.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_f8a8741f4282fcd5603e23a8a84cdf40 |
| identifier_str_mv | 10.3389/fnhum.2025.1526554.s001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28367321 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Data Sheet 1_The use of low-density EEG for the classification of PPA and MCI.docxPanteleimon Chriskos (17251084)Kyriaki Neophytou (6377588)Christos A. Frantzidis (10780023)Jessica Gallegos (20688188)Alexandros Afthinos (13806361)Chiadi U. Onyike (20688191)Argye Hillis (10471100)Panagiotis D. Bamidis (9988646)Kyrana Tsapkini (6831107)Neurocognitive Patterns and Neural Networksprimary progressive aphasiamild cognitive impairmentclassificationelectroencephalography (EEG)functional connectivityenergy rhythmsObjective<p>Dissociating Primary Progressive Aphasia (PPA) from Mild Cognitive Impairment (MCI) is an important, yet challenging task. Given the need for low-cost and time-efficient classification, we used low-density electroencephalography (EEG) recordings to automatically classify PPA, MCI and healthy control (HC) individuals. To the best of our knowledge, this is the first attempt to classify individuals from these three populations at the same time.</p>Methods<p>We collected three-minute EEG recordings with an 8-channel system from eight MCI, fourteen PPA and eight HC individuals. Utilizing the Relative Wavelet Entropy method, we derived (i) functional connectivity, (ii) graph theory metrics and extracted (iii) various energy rhythms. Features from all three sources were used for classification. The k-Nearest Neighbor and Support Vector Machines classifiers were used.</p>Results<p>A 100% individual classification accuracy was achieved in the HC-MCI, HC-PPA, and MCI-PPA comparisons, and a 77.78% accuracy in the HC-MCI-PPA comparison.</p>Conclusion<p>We showed for the first time that successful automatic classification between HC, MCI and PPA is possible with short, low-density EEG recordings. Despite methodological limitations of the current study, these results have important implications for clinical practice since they show that fast, low-cost and accurate disease diagnosis of these disorders is possible. Future studies need to establish the generalizability of the current findings with larger sample sizes and the efficient use of this methodology in a clinical setting.</p>2025-02-07T06:48:54ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.3389/fnhum.2025.1526554.s001https://figshare.com/articles/dataset/Data_Sheet_1_The_use_of_low-density_EEG_for_the_classification_of_PPA_and_MCI_docx/28367321CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/283673212025-02-07T06:48:54Z |
| spellingShingle | Data Sheet 1_The use of low-density EEG for the classification of PPA and MCI.docx Panteleimon Chriskos (17251084) Neurocognitive Patterns and Neural Networks primary progressive aphasia mild cognitive impairment classification electroencephalography (EEG) functional connectivity energy rhythms |
| status_str | publishedVersion |
| title | Data Sheet 1_The use of low-density EEG for the classification of PPA and MCI.docx |
| title_full | Data Sheet 1_The use of low-density EEG for the classification of PPA and MCI.docx |
| title_fullStr | Data Sheet 1_The use of low-density EEG for the classification of PPA and MCI.docx |
| title_full_unstemmed | Data Sheet 1_The use of low-density EEG for the classification of PPA and MCI.docx |
| title_short | Data Sheet 1_The use of low-density EEG for the classification of PPA and MCI.docx |
| title_sort | Data Sheet 1_The use of low-density EEG for the classification of PPA and MCI.docx |
| topic | Neurocognitive Patterns and Neural Networks primary progressive aphasia mild cognitive impairment classification electroencephalography (EEG) functional connectivity energy rhythms |