The use of low-density EEG for the classification of PPA and MCI

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.

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Publication Details

Journal
Frontiers in Human Neuroscience
Published
2025-02-07
DOI
https://doi.org/10.3389/fnhum.2025.1526554
Citations
1
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
1.43

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article

The use of low-density EEG for the classification of PPA and MCI

Argye E. Hillis, Alexandros Afthinos, Kyrana Tsapkini, Chiadi U. Onyike et al.
1 citations
Frontiers in Human Neuroscience
EEG and Brain-Computer Interfaces
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article

The use of low-density EEG for the classification of PPA and MCI

Argye E. Hillis, Alexandros Afthinos, Kyrana Tsapkini, Chiadi U. Onyike, Kyriaki Neophytou, Panteleimon Chriskos, Panagiotis D. Bamidis, Jessica Gallegos, Christos A. Frantzidιs
article en
1 citations

Abstract

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.

Frontiers in Human NeuroscienceVol. 19
Johns Hopkins University (US), Aristotle University of Thessaloniki (GR), Cooper Medical School of Rowan University (US), University of Lincoln (GB)
National Institutes of Health, National Institute on Aging
Affordable and clean energy
Openalex Percentile: Top 23%
EEG and Brain-Computer Interfaces
1.43
DOIOpen Access PDFOpenAlex
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The use of low-density EEG for the classification of PPA and MCI — Argye E. Hillis, Alexandros Afthinos, et al. · Frontiers in Human Neuroscience (2025) | TGRS Research Map | TGRS