Multi-Entropy Feature Concatenation for Data-Efficient Cross-Subject Classification of Alzheimer’s Disease and Frontotemporal Dementia from Single-Channel EEG

Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are neurodegenerative disorders where early detection is vital. However, the need for long-term monitoring is incompatible with data-scarce settings, and methods trained on one subject often fail on another due to cross-subject variability....

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Bibliographic Details

Main Authors:	Jiawen Li, Chen Ling, Weidong Zhang, Jujian Lv, Xianglei Hu, Kaihan Lin, Jun Yuan, Shuang Zhang, Rongjun Chen
Format:	Artigo
Language:	Inglês
Published:	MDPI AG 2026-02-01
Series:	Entropy
Subjects:	electroencephalography (EEG) entropy feature concatenation Alzheimer’s disease (AD) frontotemporal dementia (FTD) biomedical engineering
Online Access:	https://www.mdpi.com/1099-4300/28/2/212
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