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...

Full description

Saved in:

Bibliographic Details
Main Author:	Panteleimon Chriskos (17251084) (author)
Other Authors:	Kyriaki Neophytou (6377588) (author), Christos A. Frantzidis (10780023) (author), Jessica Gallegos (20688188) (author), Alexandros Afthinos (13806361) (author), Chiadi U. Onyike (20688191) (author), Argye Hillis (10471100) (author), Panagiotis D. Bamidis (9988646) (author), Kyrana Tsapkini (6831107) (author)
Published:	2025
Subjects:	Neurocognitive Patterns and Neural Networks primary progressive aphasia mild cognitive impairment classification electroencephalography (EEG) functional connectivity energy rhythms
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!

Data Sheet 1_The use of low-density EEG for the classification of PPA and MCI.docx

Similar Items