Multi-Entropy Feature Concatenation for Data-Efficient Cross-Subject Classification of Alzheimer's Disease and Frontotemporal Dementia from Single-Channel EEG.
- DOI
- 10.3390/e28020212
- Published
- 2026 Feb 12
- Container
- Entropy (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/e28020212,
title = {Multi-Entropy Feature Concatenation for Data-Efficient Cross-Subject Classification of Alzheimer's Disease and Frontotemporal Dementia from Single-Channel EEG.},
author = {Li J and Ling C and Zhang W and Lv J and Hu X and Lin K and Yuan J and Zhang S and Chen R},
year = {2026},
journal = {Entropy (Basel, Switzerland)},
doi = {10.3390/e28020212},
url = {https://doi.org/10.3390/e28020212}
}RIS
TY - JOUR TI - Multi-Entropy Feature Concatenation for Data-Efficient Cross-Subject Classification of Alzheimer's Disease and Frontotemporal Dementia from Single-Channel EEG. AU - Li J AU - Ling C AU - Zhang W AU - Lv J AU - Hu X AU - Lin K AU - Yuan J AU - Zhang S AU - Chen R PY - 2026 JO - Entropy (Basel, Switzerland) DO - 10.3390/e28020212 UR - https://doi.org/10.3390/e28020212 ER -
APA
J, L., C, L., W, Z., J, L., X, H., K, L., J, Y., S, Z., & R, C. (2026). Multi-Entropy Feature Concatenation for Data-Efficient Cross-Subject Classification of Alzheimer's Disease and Frontotemporal Dementia from Single-Channel EEG.. Entropy (Basel, Switzerland). https://doi.org/10.3390/e28020212
Source records
- pubmed · retrieved 2026-09-27T11:41:38.390Z